Analysis of transportation and logistics processes for soybeans in

Transcription

Analysis of transportation and logistics processes
for soybeans in Brazil
A case study of selected production regions
Olivia Fliehr
Thünen Working Paper 4
Olivia Fliehr
Thünen Institute of Farm Economics
Bundesallee 50
38116 Braunschweig
Germany
Contact person in the institute:
Stefan Ellsiepen
Thünen Institute of Farm Economics
Bundesallee 50
38116 Braunschweig
Germany
E-Mail: [email protected]
Fon: +49-531-596-5144
Fax: +49-531-596-5199
Braunschweig/Germany, July 2013
Acknowledgments
i
Acknowledgments
My grateful thanks are given to
for this research work.
Cash Crop that provided funding and assistance
Particularly, I would like to express my very great appreciation to Dipl.-Ing. agr. Stefan Ellsiepen, my
research supervisor at the THÜNEN INSTITUTE OF FARM ECONOMICS (Brunswick, Germany), for his patient
guidance, great encouragement, valuable inputs and useful critiques of this research work.
I would like to express my gratitude to Prof. Dr. Harald Grethe, who was willing to supervise my thesis
on the part of the UNIVERSITY OF HOHENHEIM, and to Dr. Yelto Zimmer from the THÜNEN INSTITUTE OF FARM
ECONOMICS, who initiated and guided this thesis.
Professional advice given by my supervisor Dipl.-Ing. agr. Stefan Ellsiepen and other members of the
THÜNEN INSTITUTE OF FARM ECONOMICS was of great assistance for the overall research progress. Many
thanks to Dr. rer. nat. André Gützloe from the UNIVERSITY OF HOHENHEIM, who provided helpful technical
support as regards CO2 accounting.
I would like to thank the Brazilian institutions ABIOVE, APROSOJA, CEPEA, ESALQ-LOG, IMEA for their assistance with the collection of my data and their help to connect me to experts from the soybean logistics sector. My grateful thanks are extended to the experts, who agreed to the interview and shared
with me the important information and data on which this thesis is based. Thank you for your willingness and valuable time spent for our discussion.
I am particularly grateful for the assistance given by my sister Judith as my proofreader and her great
dedication. I wish to offer special thanks to my mother, my sister, my great family and my friends for
giving me power and strengthening my perseverance throughout the whole period of researching
and thesis writing.
M. Sc. Olivia Fliehr
Summary/Zusammenfassung
iii
Summary
This study examines the relevance of Brazilian domestic transportation and logistics processes for
agricultural bulk exports in terms of costs and CO2 emissions between farm gate and seaport,
using the example of soybeans. To estimate the impact of logistics and transportation on the
competitiveness of soybeans in a national comparison, export processes starting in four selected
regions within the Sorriso-Santos corridor and travelling to the principal seaport for soybean exports in Santos-SP are evaluated. The analysis is based on agricultural statistics and data from
field research as well as on information from expert interviews. Experts belong to the transportation and logistics sector, to the sector of agricultural production as well as to relating associations
and research institutions. The results provide insight into the development and status quo of the
domestic soybean market and infrastructure for transportation and storage of bulk materials.
Structure and actors involved in the domestic supply chain are presented in a market analysis. A
cost analysis elucidates a clear correlation of the export price (fob Santos) with the producer
price (at the local spot market). The prices differ basically by the freight rates for domestic transportation. An impact assessment of CO2 emissions shows that rail transportation is in the specific
cases not more efficient in terms of CO2 emissions than road transportation. This is due to a low
utilization of the transport capacity on the train’s return trips. The survey results also show the
current bottlenecks and the potential and trends for the future development of the Brazilian logistics and transport sector for agricultural bulk products. The importance of efficient logistics
and transportation processes for the competitiveness of soybeans on the international market is
increasing with the relocation of the major soybean production areas into remote rural areas
with underdeveloped infrastructure. The nationwide deficient network of transportation and
storage infrastructure as well as of transshipment terminals leads to increased costs and capacity
constraints. Monopoly-like structures and insufficient capacities in the regional rail system also
exacerbate the problem of high freight rates. Recent public and private initiatives to improve the
national infrastructure, particularly in the ports in northern Brazil, could lead to large improvements in the soybean logistics.
JEL: Q13, Q17, Q50
Keywords: Brazil, soybeans, infrastructure, logistics, transport, logistics costs, transportation costs,
CO2-emissions
Zusammenfassung
In der vorliegenden Studie erfolgt eine Analyse der brasilianischen Logistik und des Inlandstransports für agrarische Schüttgüter am Beispiel von Sojabohnen. Um den Einfluss von Transport und
Logistik auf die Wettbewerbsfähigkeit der Sojaproduktion in Brasilien zu ermitteln, wurde eine
Markt- und Kostenanalyse für vier Anbaugebiete im Sorriso-Santos-Korridor mit abnehmenden
Transportdistanzen zum Hauptexporthafen von Soja in Santos-SP durchgeführt. Hierfür wurden
agrarstatistische Daten sowie Ergebnisse von Befragungen unter Experten aus dem Transportund Logistiksektor, aus der landwirtschaftlichen Produktion sowie aus Verbänden und der Wissenschaft ausgewertet. Die Ergebnisse geben dabei Aufschluss über Entwicklung und Status quo
des Sojamarktes und der inländischen Infrastruktur für Transport und Lagerung von Schüttgütern.
iv
In einer Marktanalyse werden Struktur und beteiligte Akteure der inländischen Lieferkette dargestellt. Eine Kostenanalyse verdeutlicht, dass ein klarer Zusammenhang von Hafenpreis (fob Santos) und Produzentenpreis (am lokalen Spotmarkt) besteht. Die Preise unterscheiden sich im Wesentlichen nur durch die Frachtraten. Eine CO2-Bilanzierung verdeutlicht, dass der Schienentransport aufgrund geringer Auslastungen der Transportkapazitäten auf den Rückfahrten gegenüber
dem Straßentransport nicht emissionseffizienter ist. Die Umfrageergebnisse zeigen darüber hinaus die aktuellen Engpässe sowie weitere Potenziale und Tendenzen zur künftigen Entwicklung
des brasilianischen Logistik- und Transportsektors für Agrarerzeugnisse auf. Als zweitgrößter Exporteur auf dem Weltmarkt gehört Brasilien zu den Hauptversorgern der weltweiten Sojanachfrage. Der Einfluss von Logistik und Transport auf die Wettbewerbsfähigkeit von Sojabohnen auf
dem internationalen Markt nimmt durch die Verlagerung der Hauptanbaugebiete in küstenferne
und infrastrukturarme ländliche Räume zu. Die allgemein defizitäre Infrastruktur von Transportnetz, Lager und Verladeterminals führt zu erhöhten Kosten und Kapazitätsengpässen. Monopolähnliche Strukturen und unzureichende Kapazitäten im regionalen Eisenbahnbetrieb verschärfen
zudem das Problem hoher Frachtpreise. Investitionen in den Ausbau der Infrastruktur, besonders
in den Häfen im nördlichen Brasilien, mit privaten und öffentlichen Mitteln versprechen eine
deutliche Optimierung der Logistik.
JEL: Q13, Q17, Q50
Schlüsselwörter: Brasilien, Soja, Infrastruktur, Logistik, Transport, Logistikkosten, Transportkosten,
CO2-Emissionen
Table of Contents
I
Table of contents
Acknowledgments
i
iii
1
Introduction
1
1.1
Statement of the problem
1
1.2
Research objectives
2
1.3
Conceptual framework
3
The Brazilian soybean market
5
2.1
Production development, trade and outlook
5
2.2
Selected regions relevant for the export
2.2.1
Sorriso-MT
2.2.2
Rondonópolis-MT
2.2.3
Rio Verde-GO
2.2.4
Barretos-SP
2.2.5
Santos port
11
13
15
16
17
19
2.3
Characteristics of soybean logistics and transportation in Brazil
2.3.1
Road transportation
2.3.2
Rail transportation
2.3.3
Water transportation
2.3.4
Storage
2.3.5
Policy and investments
2.3.6
Climatic impact of soybean transportation
21
23
25
27
28
30
31
2
3
4
Methodology
35
3.1
Own research approach
35
3.2
Market analysis
35
3.3
Case studies
36
3.4
Semi-structured interview
37
3.5
Cost reporting
38
3.6
Greenhouse gas inventory
41
Analysis of the Brazilian soybean export value chain
45
4.1
46
46
Market analysis
4.1.1
Agents
II
Table of Contents
4.1.2
4.1.3
5
Logistics processes
4.1.2.1 Grain origination
4.1.2.2 Warehousing
4.1.2.3 Transportation
4.1.2.4 Port
Soybean season
48
48
49
50
54
55
4.2
Cost analysis for four production regions
4.2.1
Cost positions and cost structure
4.2.2
Cost reporting
4.2.2.1 Total logistics costs
4.2.2.2 Cost of truck transport
4.2.2.3 Sensitivity analysis
56
56
61
62
64
68
4.3
Impact assessment of soybean transports on climate change and interpretation
70
Bottlenecks and potential developments
75
5.1
Road transportation
75
5.2
Rail transportation
76
5.3
77
5.4
Storage capacities
77
5.5
Port system
78
5.6
Truck drivers law
79
6
Discussion of results
83
7
Summary
87
8
References
91
Personal contact
Appendices
107
A1-A47
List of Tables
III
List of Tables
Table 2.1:
Major soybean producing states in Brazil (2011)
8
Table 2.2:
Sorriso - soybean production and exports (2006-2011)
14
Table 2.3:
Rondonópolis - soybean production and exports (2006-2011)
15
Table 2.4:
Rio Verde - soybean production and exports (2006-2011)
17
Table 2.5:
Barretos - soybean production and exports (2006-2011)
19
Table 2.6:
Santos - soybean export volumes of selected regions (mmt)
20
Table 2.7:
Comparison of transport modalities relevant for soybean shipping
22
Table 2.8:
Brazilian road system extension (km)
24
Table 2.9:
Greenhouse gases and sources of exhaust emissions
32
Table 3.1:
Expert interviews by category
37
Table 3.2:
Calculation method
38
Table 4.1:
Storage capacities in selected regions vs. production output of
soybeans and corn (2011)
50
Table 4.2:
Transit time from selected regions and average speed driven.
52
Table 4.3:
Soybean crop calendar
55
Table 4.4:
Composition of road freight rate
59
Table 4.5:
Logistics cost calculation for selected regions (April 2011)
62
Table 4.6:
Total logistics costs - comparison of selected regions (R$/tkm)
63
Table 4.7:
Fixed costs of model
65
Table 5.1:
Medium waiting time at port terminals (h) in Santos (2009, 2010).
78
IV
List of Figures
List of Figures
Figure 2.1:
Soybean complex - structure of the industry.
6
Figure 2.2:
Soybean production of the world's leading soybean producers
(1999-2012)
7
Figure 2.3:
Brazil - demand and supply
9
Figure 2.4:
Brazilian soybean exports (1991-2011)
10
Figure 2.5:
Sorriso-Santos soybean export corridor
12
Figure 2.6:
Sorriso - soybean exports (2006-2011)
14
Figure 2.7:
Rondonópolis - Exports by product (2007-2010)
16
Figure 2.8:
Rio Verde - corn and soybean production (1990-2010)
17
Figure 2.9:
Area harvested in the county of Barretos (%)
18
Figure 2.10:
Connection of Santos port to the national transport system
20
Figure 2.11:
Transport matrix for soybean shipping
23
Figure 2.12:
Impact of road condition on operational costs
24
Figure 2.13:
Demand for soybean transportation via the waterway (2010-2011)
27
Figure 2.14:
Storage capacities vs. production per macro region (2011/12)
29
Figure 2.15:
Transport matrix - 2005 and 2025 (%)
31
Figure 2.16:
Amount of kgCO2eq emitted during the life cycle of soybeans produced
in the Center West
33
Figure 3.1:
Transportation costs
40
Figure 3.2:
Product life cycle
42
Figure 4.1:
Exemplary route integrating selected production regions
45
Figure 4.2:
Market share by company size
47
Figure 4.3:
Exemplary export process from farm to seaport.
48
Figure 4.4:
Truck model Bitrem
51
Figure 4.5:
Intermodal export corridors
52
Figure 4.6:
Wagon for bulk transports.
53
Figure 4.7:
Brazil - monthly soybean exports (2009-2011)
56
Figure 4.8:
Logistics cost structure
56
Figure 4.9:
Freight rates for selected regions (2011-2012)
58
Figure 4.10:
Exemplary cost structure for operating a warehouse
60
List of Figures
Figure 4.11:
V
Comparison of theoretical and real producer price
(April 2011, September 2011)
64
Figure 4.12:
Costs of road transportation (March 2011)
66
Figure 4.13:
Comparison of transport costs and freight rates
67
Figure 4.14:
Sensitivity analysis - maintenance costs
68
Figure 4.15:
Sensitivity analysis - interest rate
69
Figure 4.16:
Sensitivity analysis - fuel price
70
Figure 4.17:
GHG emissions through road transport over selected distances
(kgCO2eq/t)
71
GHG emissions during soybean exportation for selected transportation
routes and modalities
72
Figure 4.19:
Sensitivity analysis - fuel efficiency
73
Figure 5.1:
Development of selected freight rates (2010-2012)
80
Figure 4.18:
VI
List of Appendices
List of Appendices
Appendix 1:
Exemplary calculations of soybean production costs – international
comparison
3
Appendix 2:
Soybean price – domestic price and CBOT quotation (US$/t, 1995-2005)
4
Appendix 3:
Brazil – macroregions and states
4
Appendix 4:
Climate charts – Brazil
5
Appendix 5 und 6: Soybean exports and imports by country (2008-2013)
6
Appendix 7:
World soybean consumption and exports (1960-2012)
6
Appendix 8:
Cerrado region
7
Appendix 9:
Brazilian agricultural policy development (1965-2005)
8
Appendix 10:
Projection of world soybean production
9
Appendix 11:
GDP and soy production of selected regions (2009)
9
Appendix 12:
Brazilian soybean exports by port (2009-2011)
10
Appendix 13:
Soybean exports via Santos port (2010-2011)
10
Appendix 14:
Privatization efforts of the Brazilian government in the 1990s
11
Appendix 15:
Transport costs related to modality, distance and relative fuel efficiency
11
Appendix 16:
Mean values of freights for soybeans
12
Appendix 17:
State of the national road network by state (2011)
13
Appendix 18:
Brazilian cargo railways
14
Appendix 19:
Extension of the rail network under concession by operator
14
Appendix 20:
Inland waterways and navigable rivers
15
Appendix 21:
Soybeans - conditions at storage
16
Appendix 22:
Brazilian standard values for soybean exports
16
Appendix 23:
Total volume of agricultural bulk goods produced (t) in 2011 vs. storage
capacities (status quo 12/2012)
17
Appendix 24
Distribution of warehousing capacities by location
17
Appendix 25:
Development of investments in infrastructure of federal government
(1975-2011) (Investments/GDP in %)
18
Appendix 26:
PNLT investments (2008-2023)
18
Appendix 27:
Distribution of global GHG emissions by sector
19
Appendix 28:
Phases of the Airquality Program for Automotive Vehicles
19
List of Appendices
VII
Appendix 29:
Emission factors considered
20
Appendix 30:
Frequently travelled warehouse-port routes
20
Appendix 31:
Tower-silo storage on farm in Alto Araguaia-MT
21
Appendix 32:
Tolled highway tracks on exemplary route
21
Appendix 33
Exemplary roads (MT, GO) Source: Photos taken by author.
22
Appendix 34:
Volume transported on the ALLMN railroad (mmt)
23
Appendix 35:
Warehousing fees (2010)
24
Appendix 36:
Soybean world market prices - time series 1995-2012
25
Appendix 37:
Local spot market prices (2007-2012)
26
Appendix 38:
Calculation of total logistics costs of soybean exports for selected regions
(September 2011)
26
Appendix 39:
Local fuel prices of selected regions (2004-2011)
27
Appendix 40:
Fuel efficiency versus fleet age
27
Appendix 41:
GHG intensity of selected maritime freight transport modes
28
Appendix 42:
Development of Brazilian road network
28
Appendix 43:
Development of Brazilian railroad network
28
Appendix 44:
Development of the Brazilian waterway network
28
Appendix 45:
National export destinations of soybeans
29
Appendix 46
Exchange rate - Brazilian real (R$) / US dollar (US$)(2000-2012)
30
Appendix 47:
Fob price and selected producer prices
31
Appendix 48:
Freight rates for selected routes (2011-2012)
32
Appendix 49:
Calculation of total logistics costs of soybean exports for selected (2012)
33
Appendix 50:
Calculation of transport costs for soybean shipping from selected
regions to Santos port
35
Appendix 51:
37
Appendix 52:
Sensitivity analysis - interest rate.
39
Appendix 53
Sensitivity analysis - fuel costs
42
Appendix 54:
Calculation of GHG emissions of soybean shipping from selected
regions to Santos port
45
Sensitivity analysis - increased loading factor (40%) and higher fuel
consumption
45
Sensitivity analysis - reduced fuel consumption
46
Appendix 55:
Appendix 56:
VIII
List of Appendices
Appendix 57:
Sensitivity analysis - decreased emission factor
46
Appendix 58:
Interview guideline
47
List of Abbreviations
IX
ABIOVE
Brazilian Vegetable Oil Industries Association
ALL
América Latina Logística (Brazilian railway operator)
ALLMN
ALL Malha Norte (railroad MT to SP operated by ALL)
ALLMP
ALL Malha Paulista (railroad SP operated by ALL)
AM
Amazonas (federal state of Brazil)
ANTT
National Transportation Agency
CEPEA
Center for Advanced Studies on Applied Economics of ESALQ
cif
cost insurance freight
CNT
National Confederation of Transportation
CO2eq
CO2 equivalents
Eq.
Equation
ES
Espirito Santo (federal state of Brazil)
ESALQ
College of the Luiz Queiroz College of Agriculture at the University of São Paulo
ESALQ-LOG
ESALQ Group of Research and Extension in Agroindustrial Logistics
fob
free on board
GDP
gross domestic product
GHG
greenhouse gas(es)
GWP
global warming potential
ha
hectare(s)
IBGE
Brazilian Institute of Geography and Statistics
ICMS
Tax over commercialization and services
IMEA
Institute of Mato Grosso Agricultural Economics
kg
kilogram(s)
l
liter
IPCC
Intergovernmental Panel on Climate Change
LCA
Life Cycle Assessment
mha
million hectares
mm
millimeters
mmt
million metric tons
X
MS
Mato Grosso do Sul (federal state of Brazil)
MT
Mato Grosso (federal state of Brazil)
MAPA
Ministry of Agriculture, Livestock and Food Supply
MDIC
Ministry of Development, Industry and Foreign Trade
PA
Pará (federal state of Brazil)
PAC
Growth Acceleration Plan
PNLT
National Plan for Logistics and Transportation
PR
Paraná (federal state of Brazil)
PSD
USDA Database Production, Supply and Distribution Online
RO
Rondônia (federal state of Brazil)
RS
Rio Grande do Sul (federal state of Brazil)
Secex
Brazilian Secretariat of Foreign Trade
SP
São Paulo (federal state of Brazil)
t
metric ton(s)
tha
thousand hectares
tkm
tonne kilometer(s)
USA
United States of America
USDA
United States Department of Agriculture
Chapter 1
Introduction
1
Introduction
1.1
Statement of the problem
1
Brazil is a country with a competitive advantage in agricultural production. Low on-farm production costs and land abundance granted the country this competitive advantage and helped to
make Brazil an internationally competitive producer and exporter of soybeans (see appendix 1),
as various studies emphasized (CASTILLO/VENCOVSKY/BRAGA, 2011; KUSSANO/BATALHA, 2009; FILARDO et
al., 2006; USDA, 2012). But long distances between production sites and domestic destination
require a high participation of transportation and logistics services within the process chain of
soybean exports. This includes transportation from farm to warehouse and to the seaport as well
as storage and transshipments at intermodal transportation1 terminals and at the seaport.
Because of a low value per unit of agricultural goods, transportation may account for a large
share of the total costs of soybean exports (CASTILLO/VENCOVSKY/BRAGA, 2011, p. 21). Increases in
transportation costs are directly passed on to the weaker market player, so that rising transportation costs have the effect of decreased prices paid to producers because of their lack of market
power (USDA, 2010, p. 3). A well functioning transportation service sector is crucial in order to
keep transportation costs at a low level and preserve international competitiveness. A lacking
infrastructure, however, may generate high costs for transportation and logistics as well as lead
to congestion and consequently longer delivery time in peak harvest seasons. The infrastructure
is hence a determinant of the competitive success of the agricultural sector (CAIXETA-FILHO, 2006,
p. 1). As soybeans are a low value-added bulk good, the domestic price of which is closely related
to the international market price quoted at the CHICAGO BOARD OF TRADE [CBOT] (MAPA, 2007,
pp. 45) (see appendix 2), elevated costs of logistics arising from long distance transports
consequently reduce the price received by the producers in the remote production areas
decisively.
The Brazilian oilseed production expanded in the last decades into rural areas remote from the
southern seaports, which cover the major part of the Brazilian soybean exports. It can be inferentially assumed that farm gate prices are strongly affected by transportation costs. Inefficiencies in
transportation and logistics systems can hence prejudice the producers and put pressure on prices at all stages of the process chain, counteracting the competitiveness of the Brazilian soybean
business (CAIXETA-FILHO/CARVALHO/BRANCO, 2007).
Negative externalities on climate change may also arise when soybeans are transported over long
distances owing to large volumes of exhaust emissions that result from high fuel consumption.
Due to the rising concern of consumers and policy makers about the climatic impact of product
1
According to VAHRENKAMP (2007, p. 309), intermodality is the combination of various transportation modes to transport
a good to its destination.
2
Chapter 1
Introduction
chains, the amount of emissions of greenhouse gases [GHG] may become a further factor of influence on competitiveness2 of soybeans. Few efforts have been made in Brazil to report or reduce the global warming potential [GWP]. There is a great need to raise the environmental
awareness of the participants of the soybean business and report exhaust emissions for minimizing the climatic impact.
With the doubling of the export volume between 2001/02 (16.07 mmt) and 2011/12 (32.08 mmt)
transportation and logistics processes gained notably weight in Brazil (USDA PSD, 2013). With the
increasing volumes that had to be moved, the demand for transportation and logistics services
was concurrently boosted. As the world's second biggest producer and exporter of soybeans, the
country is an important supplier for the world soybean market. It contributed a share of 28 %
(66.50 mmt; cf. USA: 84.19 mmt, 35 %) to the world soybean production (238.73 mmt) and accounted for 35 % (32.08 mmt) of the world export volume in 2011/12, (cf. USA: 37.06 mmt, 41 %)
(USDA PSD, 2013). Projections estimated that Brazil would become the globally leading producer
and exporter within the next decade (USDA, 2012). In this context, German importers of soybean
products emphasized the need for an improvement of the Brazilian infrastructure in order to ensure delivery liability (STRAUSS, 2012; DRESCHER, 2012; HÜNER, 2012). In order to safeguard supply
and allow competitive prices for producers, logistics and transportation processes are a key factor for the Brazilian soy sector.
1.2
Research objectives
This thesis aims to analyze the relevance of domestic transportation and logistics processes of
soybean exports in terms of costs and CO2 emissions between farm gate and seaport for selected
regions in Brazil. For this purpose, the characteristics of the soybean export process shall be elucidated at first by answering the question:
1.
How is the Brazilian market of soybean logistics structured, who are the participants and
which are the process stages of the export value chain on national level?
This information provides the basis to evaluate the influence of the logistics processes on the
competitiveness of the Brazilian soybeans. To that effect, the further questions that shall be answered within the framework of a market and cost analysis are:
2.
2
How is the logistics cost structure composed and which are the key cost elements?
DOHLMANN/SCHNEPF/BOLLING (2001, p. 16) define the competitiveness of the soy sector as a composition of various factors, which include relative resource endowments and agro-climatic conditions, as well as the impact of macroeconomic policies, e.g., investment and energy costs and availability, sector-specific policies like tax incentives and credit subsidies, infrastructure for storage and transportation, and of supporting institutions that help markets to work effectively
(e.g., credit, regulatory, news and information, etc.).
Chapter 1
Introduction
3
3.
Which is the climatic impact of domestic soybean transports from warehouse to seaport?
4.
How can costs and CO2 emissions of the logistics and transportation processes influence the
competitiveness of the soybeans produced in the studied regions?
In order to evaluate future developments of the market, regarding the rising soybean volumes
traded and the simultaneously increasing demand for transportation and logistics services, it has
to be conclusively considered:
5.
1.3
What are the challenges and potentials of the Brazilian transport sector for soybean exports?
Conceptual framework
The influence of the logistics and transportation processes on the competitiveness of the Brazilian soybeans shall be estimated by exemplarily analyzing the soybean export value chains starting
in four selected production regions and ending at the seaport of Santos-SP. For that, four case
studies, which incorporate the major Brazilian soybean production region Sorriso and the principal soybean export port Santos, are conducted to draw a picture of the current situation of soybean logistics and to analyze the bottlenecks and potentials.
Essential knowledge of the market for soybeans in Brazil is at first provided in chapter two. This
chapter presents information on production, consumption and trade, and describes exemplarily
the characteristics of four selected soybean production regions, which occupy a central position
in the domestic soybean business. It further furnishes information on the status quo of the infrastructural system and agricultural policy measures to increase investments. Subsequently, chapter three explains the performed research approach and defines the applied methods.
The results of the field research and the data analysis are presented in chapter four and five. In
chapter four a market and a cost analysis are exemplarily performed for the selected soybean
production regions described in chapter two. Within the framework of the market analysis the
structure of the soybean logistics and transport market is studied. In the cost analysis, the logistics cost positions of the export process are spotted and described in detail. The reporting of the
cost structure is followed by an analysis of the logistics costs. Particular attention is paid to the
costs of road transportation, as it is assumed that these represent a major element of total logistics costs. That way, the relevance of the transportation and logistics costs for the farm gate
price, i.e., the price that the producer receives is estimated. The importance of single factors of
influence on the transport costs shall be evaluated by performing a sensitivity analysis. To assess
the impact of domestic transports of soybean exports on climate change, the emissions of greenhouse gases are reported and assessed in a GHG inventory. In order to examine the effects of
changes of specific factors on global warming potential a sensitivity analysis is performed subsequently.
4
Chapter 1
Introduction
In chapter five challenges and potentials of the Brazilian soybean logistics are assessed, considering the bottlenecks within the logistics processes and the projects to improve the infrastructural
network. The results are discussed in chapter six. In the final section of chapter seven results are
resumed.
Chapter 2
2
5
To understand the relevance of the subject and as a basis for the analyses in chapter four, it is
necessary to look at the current situation of the global and national soybean production and to
elucidate the status quo of the Brazilian infrastructure. The following section provides basic information on soybean production and exports on international, national and regional level. It
further addresses logistics issues that refer to the infrastructural conditions, related agricultural
policy and climatic impacts of the soybean export process.
2.1
Production development, trade and outlook
With a gross domestic product [GDP] of 4.14 trillion R$ in 2011 (IPEA, 2012), Brazil ranks among
the world’s eight leading economies (CIA, 2012). Agricultural production valued with
195.60 billion R$ accounted for approximately five percent of the GDP (IBGE, 2011, p. 16; WORLD
BANK, 2012). The three products generating the highest value of the national agricultural production are, according to MAPA (2012), soybeans (25 %), sugarcane (18 %) and corn (12 %). Soybeans
contributed 50.37 billion R$ to the national GDP of 2011 (IBGE, 2011, p. 17). The agricultural
commodity is furthermore a pivotal element of the Brazilian trade balance (MDIC, 2012). With
32.99 mmt being exported in 2011 soybeans represented a value of 16.33 billion US$1 or six percent of the value of Brazil's total exports (256.04 US$).
The soybean complex
Soybean (Glycine max (L.) Merrill) is an annual crop of the legume (LEGUMINOSAE) family (FRANKE,
1994, p. 271). It is a seasonal crop with a cultivation period of 80 to 200 days, which depends on
variety, climate and precipitation. Fertile soils and a temperate to tropical climate with temperatures between 27°C and 32°C as well as steady rainfalls of minimum 500 mm are required for
optimum growth and productivity (FRANKE, 1994, p. 273). The soybean cropping regions in south
and central west Brazil (see appendix 3-4) meet these climatic conditions. The soils of the South
are naturally fertile, whereas soil productivity in the Center West must be enhanced by adding
fertilizer, especially lime. Double cropping with soybeans and, e.g., corn is common in this region.
No-till production is widely practiced to minimize erosion of the fragile soils and to reduce longterm costs from soil degradation (FLASKERUD, 2003, p. 4; DIAS, 2012; USITC, 2012, pp 2-4, 6-15).
The structure of the soybean complex is represented in figure 2.1. It comprises the production
chains of unprocessed soybeans and processed soybeans in form of soybean oil and soybean
meal. With an average oil content of 20 % on dry-weight basis, soybeans serve as a source of
1
As the value was reported by MDIC (2012) in US$ without citing the applied exchange rate, the value was adopted in
US$. This holds good for all values reported in US$ in the text.
6
Chapter 2
vegetable oil. They are used for animal fodder, mostly in form of toasted soy meal2 that can contain up to 54 % of protein (ENDRES, 2001, p. 5). The soybean suits industrial purposes like energy
and textile production. The principal derived products are meat and - to a rather limited degree energy in form of, e.g., biodiesel (GOLDSMITH/HIRSCH, 2006, p. 99).
Figure 2.1:
Soybean complex - structure of the industry.
Farmers
Direct use
Processors
Exports
Feed, seed, food
Meal
Oil
Industrial
Food
Food
Feed
Energy and industrial
Source: GOLDSMITH (2008, p. 119).
Global soybean production
Brazil belongs to the world’s largest soybean producers that include the United States of America
[USA], Brazil, Argentina, China and India. Holding a market share of 35 % (2011/12: 84.19 mmt),
the USA leads the world soybean production followed by Brazil (28 %; 66.50 mmt) and Argentina
(17 %; 41.00 mmt). These three producer countries USA, Brazil and Argentina (presented in figure 2.2) account for 80 % of the volume of the global soybean production. Since 2000/01 the
global production increased by 36 % (62.97 mmt), realizing a total output of 238.11 mmt in the
2011/12 season (USDA PSD, 2013).
Imports of soy products are concentrated in Asian and European countries while exports
(90.42 mmt) are headed by the USA with a market share of 41 % (2011/12: 37.06 mmt) and Brazil
that holds a market share of 35 % (32.08 mmt).
2
The term is used for soy flours and grits that "are made by grinding and screening soybean flakes either before or after
removal of the oil" (ENDRES, 2001, p. 4).
Chapter 2
Figure 2.2:
7
Soybean production of the world's leading soybean producers (1999-2012)
mmt
100
90
80
70
60
50
40
30
20
10
0
Argentina
Brazil
USA
1999
/00
2000
/01
2001
/02
2002
/03
2003
/04
2004
/05
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
(est.)
Source: Created by author. Based on data from USDA PSD (2012).
By far the biggest consumer is China with a domestic consumption of 72.07 mmt and imports of
59.23 mmt in 2011/12 (see appendix 5-6). Appendix 7 shows that world consumption and world
exports experienced a strong increase since 1996, when China accessed the market (USDA PSD,
2013, custom query).
Development of the soybean production in Brazil
Introduced from China, the first soybean cultivation in Brazil was registered in 1914 in the state
Rio Grande do Sul. Natural resource abundance and new technology during the 20th century
gave rise to the expansion of the Brazilian soybean industry . Cultivation expanded from the
southern regions into the western and northern regions of the country. Governmental incentives
for research and development during the 1950s, 1960s and 1970s encouraged the development
of new varieties and the adaption of the crop to Brazil's diverse local conditions. The increase of
the soybean production area was thereby spurred and large planes of arable land in new cropping areas in the Cerrado, an extensive savannah area in central Brazil encompassing over
200 mha (see appendix 8), were successively covered with soybeans (GOLDSMITH/HIRSCH, 2006,
pp. 97).
PARKHOMENKO (2004, p. 143) and GOLDSMITH/HIRSCH (2006, p. 97) underlined the positive effect of a
vast demand and favorable world market prices for soybeans in that time on the expansion of
soybean production in terms of both area and volume. When Brazil transitioned from military
rule to a democratic government, protective policy measures (see appendix 9) were reduced
from the late 1980s on and foreign trade increased. Higher import volumes of agricultural inputs
like fertilizer, pesticides and machinery boosted agricultural production in the 1990s. This impacted positively on the Brazilian exports of agricultural products so that besides national soybean production exports also accelerated (FLASKERUD, 2003, p. 4; GOLDSMITH/HIRSCH, 2006, p. 98;
USITC, 2012, p. 3-24).
8
Chapter 2
Production, consumption and exports
Between 1990 and 2011, the national soybean production grew with a compound annual growth
rate [CAGR] of seven percent from 19.90 mmt in 1990 to 32.82 mmt in 2000 and 74.82 mmt in
2011. The area planted with soybeans experienced a slower annual growth rate of four percent
from 11.58 mha (1990) to 24.03 mha (2011), 10.34 mha of which were added in the last decade
(IBGE, 2012). Productivity per hectare was boosted through the effective use of new technologies, which included artificial soil enrichment and soybean seeds that were engineered for a tropical climate or genetically modified (USITC, 2012, pp. 2-4, 6-15), from 1.72 t/ha in 1990 to
2.40 t/ha in 2000, and to 3.11 t/ha in 2011.
The production of soybeans is concentrated in six Brazilian states (listed in table 2.1), which account for 86 % of the national soybean output. The five principal soybean producing states belong to the South and Center West regions. 38 % (2011: 10.84 mha; 3.12 t/ha) of the total national soybean production area is located in Mato Grosso and Goiás. The area planted with soybeans in the whole Center West region increased by five percent a year from 1990 (3.89 mha) to
2011 (10.84 mha)3 while in the South the CAGR leveled two percent (1990: 6.16 mha; 2011:
9.09 mha). Due to its naturally fertile soils (FLASKERUD, 2003, p. 4), the southern state Paraná has
the highest national productivity level (3.39 t/ha). Mato Grosso, on the other hand, holds the
leading position in terms of soybean growing area and production volume. It recorded the highest area growth from 1.55 mha in 1990 to 6.46 mha in 2011 (CAGR: 7 %) and the strongest production increase from 3.06 mmt to 20.80 mmt (CAGR: 10 %). In the same period area expansion
leveled a CAGR of three percent and six percent in production increase in Paraná state, which led
the national soybean production before Mato Grosso overtook the leadership in 2000.
Table 2.1:
Mato Grosso
Paraná
Rio Grande do Sul
Goiás
Mato Grosso do Sul
Bahia
Brazil
Major soybean producing states in Brazil (2011)
Area
Production
Productivity
mha
mmt
t/ha
6.46
4.56
4.08
2.57
1.76
1.05
20.80
15.46
11.72
7.70
5.08
3.51
3.22
3.39
2.88
3.00
2.88
3.36
24.03
74.82
3.11
Source: Created by author. Data form IBGE (2012).
3
The total agricultural production area increased slightly less with a CAGR of 4.5%. The higher increase of area planted
with soybeans might be attributed to the adoption of double cropping and GM soybeans.
Chapter 2
9
At the beginning of the 1980s Mato Grosso was still a marginal player within the business
(1979/19804: 0.12 mmt; 1.67 t/ha) (CONAB, 2012) but its quantity produced increased by 187 %
between 1990 (3.06 mmt; 1.97 t/ha) and 2000 (8.77 mmt; 3.02 t/ha). It developed an aboveaverage productivity (2011: 3.22 t/ha, national average: 3.11 t/ha) and soared to being the state
with the biggest production volume (2011: 20.80 mmt). For the time being, Mato Grosso accounts for about 28 % of the national production (IBGE, 2012) and covers a share of approximately 29 % (2011: 9.67 mmt, valued with 4.77 billion US$) of the national soybean exports. 65 % of
which (2011: 6.24 mmt) are directed to China (MDIC, 2012).
Brazil's soybean production was growing faster between 2001 and 2011 (CAGR: 7 %) than the
domestic consumption level (CAGR: 5 %). Figure 2.3 represents the development of the Brazilian
demand and supply over the last decade, which shows that the share of domestic consumption
declined from 63 % (24.47 mmt) in 2001/02 to 53 % (40.11 mmt) in 2011/12. In the same period
the export volume more than doubled (ABIOVE, 2012).
Figure 2.3:
Brazil - demand and supply
mmt
90
80
Production
70
Exports
Imports
Beginning stocks
Domestic consumption
Ending stocks
60
50
40
30
20
10
0
2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14
(est.)
(est.)
Source: Created by author. Data from ABIOVE (2012).
From 1996 on, when China started to import soybeans, the Brazilian soybean exports increased
strikingly (see figure 2.4). In 2011, Brazil exported 32.99 mmt of soybeans with an equivalent value of 16.33 billion US$ (MDIC, 2012). The Brazilian soybeans are distributed to 215 global destinations, of which China is currently the biggest importer whose demanded volumes (22.10 mmt)
represented a share of 67 % of the total Brazilian soybean exports. Besides China, the European
Union is a principal market for soy products of Brazil, where Spain (2.37 mmt) and the Netherlands (1.52 mmt) are the major importers (MDIC, 2012).
4
National production volume and productivity in the 1979/80 season: 14.89 mmt, 1.70 t/ha.
10
Chapter 2
Figure 2.4:
Brazilian soybean exports (1991-2011)
mmt
35
30
25
20
15
10
5
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Source: Created by author. Data from MDIC (2012).
Outlook
According to USDA estimates, Brazil will be the largest soybean grower in the season of 2012/13
(see figure 2.2) and surpass the United States as the world's leading soybean exporter at latest by
2019/20 (USDA, 2012; APROSOJA, 2010). USDA (2012) underlined that, due to very competitive
production costs of Brazilian soybean farmers relative to U.S. producers (see appendix 1), Brazil is
able to gain enduring export supremacy. The increasing export orientation of the agribusiness
firms and cooperatives foments the soybean production volume and area extension (USITC,
2012, pxxi).
Taking into account the natural resource abundance, available technological capacities and rising
levels of investment in Brazil, there is according to APROSOJA (2012), GOLDSMITH/HIRSCH (2006, p.
97) and HUERTA/MARTINS (2002) an immense potential for further expanding the soybean production area, e.g., by converting tracts of land in permanent pasture to cropping area (FERREIRA,
2012). In a short term view, the total area planted with soybeans is projected to increase in the
2012/2013 cropping season compared to the previous year by minimum five percent (1.38 mha),
of which more than 489 tha will be added to Mato Grosso's planting area (CONAB, 2012a, p. 27).
HIGHQUEST PARTNERS (n.d.) estimated the total production volume cited by APROSOJA (2010) to
annually increase by five percent to a volume of 112 mmt in the year 2020 (see appendix 10). The
states of Mato Grosso and Paraná are estimated to remain the soybean producing states with the
highest yields (CONAB, 2012a, p. 27). The projections are based on expectations of future improvements like eased access to markets via an enhanced infrastructure, boosted domestic consumption by increased per capita income and the adaption of the cultivar to previously unsuitable regions by the development of new seeds (USITC, 2012, pp. 2-4; USSEC, 2011, pp. 17).
Chapter 2
11
Agricultural Policies
Supportive agricultural policies favored the production of raw materials with destination export.
An example of such policy measure is the Kandir Law of 1996 that promoted exports of soybeans
by exempting exports of raw and semi-elaborated products from the ICMS tax5. Prior to the law
unprocessed soybeans were taxed more than the processed soy products. This reflected a distorting disadvantage for exporting raw soybeans if its transports had to cross different states to
reach the export port, while it favored the domestic crushing in the state of origination or adjoining states (PARKHOMENKO, 2004, p. 143). The expansion of agricultural production to the Central
West pushed soybean supply far from traditional consumption regions in the South and Southeast and its well-developed transportation infrastructure (GOLDSMITH/HIRSCH 2006, p. 99). Exporting soybeans from the remote regions became more expensive in terms of fiscal and transportation costs due to long-distance shipping via different states to the seaports and a deficient infrastructure in the Central West (see chapter 2.3). With regard to the national interests of export
expansion and foreign exchange inflows, the Kandir Law exempted exports of raw and semielaborated products from the ICMS. It aimed to mitigate some of the distortionary effects of the
ICMS tax and spur soybean exports. MAPA (2007, pp. 16) reported that after the tax relieve and
the consequential diminishment of costs for exporters there was a shift of firms from domestic
processing to exporting raw soybeans6.
2.2
Selected regions relevant for the export
In order to investigate the current situation of soybean logistics in Brazil, four soybean production regions (represented in figure 2.5 by x1 to x4) and their export flows to one seaport were analyzed. The county with the major production and export volume, i.e., Sorriso in Mato Grosso, and
the seaport with the major share of its soybean exports were selected as principal points of reference for the case study. The export route from Sorriso to Santos (outlined by the arrow in figure 2.5) is henceforth referred to as the Sorriso-Santos corridor. To illustrate the influence of the
travel distance from region of origination to port on total logistics costs, further three counties,
i.e., Rondonópolis-MT, Rio Verde-GO and Barretos-SP, were selected for detailed analysis according to the following criteria:

relevance of the production region for the Brazilian soybean exports;

geographical position close to the Sorriso-Santos export corridor;

comparable distances from one location to another;
5
ICMS is a state-run value-added tax on the circulation of goods and on transportation and communication services. It is
incurred when production and utilization occur in different states, i.e. discouraging interstate commerce and exports of
value-added goods if origination and processing or exportation occur in different states (GOLDSMITH/HIRSCH, 2006,
p. 100).
6
The share of raw soybeans at total exports of the soybean complex increased from 12% at the beginning of the 1990s
to 38% at the beginning of the 2000s (MAPA, 2007, pp. 41).
12

Chapter 2
shaping of a gradient line in terms of kilometers travelled within the Sorriso-Santos corridor,
integrating all selected regions.
Figure 2.5:
Sorriso-Santos soybean export corridor
X1
X2
X3
X4
SP
Santos
x1 Sorriso
x2 Rondonópolis
x3 Rio Verde
x4 Barretos
Source: APROSOJA (2011). Modified by author.
Figure 1. Sorriso-Santos soybean export corridor.
The principal export port for products of the Brazilian soybean complex is Santos port, where
Source: APROSOJA (2011). Modified by author.
28 % of the national soybean exports are turned over. In 2011, 90 % (9.23 mmt) of this volume
was originated in the states Mato Grosso (6.05 mmt), Goiás (1.41 mmt) and São Paulo (0.86 mmt)
(MDIC, 2012). A big part of the Santos soybean exports is originated in the largest production
area of Mato Grosso that is centrally located along the highway BR-163 between Cuiabá and Sinop. The region includes the municipalities of Lucas do Rio Verde, Nova Mutum and Sorriso (IBGE, 2012). Part of its agricultural products are shipped towards the northern ports of Santarém
and São Luiz, which handle most of the soybean exports originating in north and west Brazil. But
the major share of the exported volume, which is 83 % in the case of Sorriso, leaves the country
via Santos.
Chapter 2
13
The other selected counties are also important regions for soybean production or trading. Soybean production is in Rondonópolis of marginal importance but the county is a commercial center where large volumes of soybeans and other agricultural inputs and products are traded (OSAKI, 2012). Rio Verde, however, has one of the major cultivation areas of soybeans and corn in Brazil (PARKHOMENKO, 2004, p. 148; IBGE, 2011) and accounted for three percent of the soybeans
turned over at Santos port in 2011 (MDIC, 2012). In a national comparison Barretos is a marginal
producer of soybeans and accounts only for a small volume of soybean exports. Nevertheless, it
is a representative agricultural production area of São Paulo and belongs to the state's principal
soybean production regions (FAESP, 2011). It was chosen for being situated close to the seaport
and conveniently located on the route Sorriso-Rondonópolis-Rio Verde-Santos. The selected
counties are described in detail in the subsequent subchapters.
2.2.1
Sorriso-MT
Sorriso-MT comprises an area of 9,330 km2 with a population density of 7.13 people/km2 (IBGE,
2010a)7. Sorriso is located in central Mato Grosso about 2,000 km distant from the northern and
eastern Brazilian coast (2,100 km to Santos port). Sorriso generates the highest total soybean
yields in Brazil (IBGE, 2012). To export the soybeans large distances have to be overcome. As
there is no direct access to the railroad network or waterway system, road transportation is inevitable to move the soybeans to the economic centers and transshipment terminals.
The agricultural sector contributed with 32 % (647,021 R$) to the GDP (2.04 million R$). Sorriso's
total harvested area encompassed 875,730 ha, producing a value of 1.89 billion R$ in 2011, when
Sorriso was generating the highest production value of agricultural products in Brazil (IBGE, 2011,
p. 23). Soy is the most important product of the region. With a production of 2.09 mmt in 2011
(see table 2.2), Sorriso accounted for three percent of the Brazilian soybean production volume
(74.8 mmt).
7
Most recent data available by IBGE (2010a) on GDP for the four municipalities are from 2009, data on population are
based on the population census conducted in 2007. For reasons of comparability appendix 11 represents most important numbers of 2009 on municipal, state and federal level of the selected regions.
14
Chapter 2
Table 2.2:
Sorriso - soybean production and exports (2006-2011)
Sorriso - MT
Year
2006
2007
2008
2009
2010
2011
Area
Quantity
Value
Exports
Export share
ha
t
R$
t
%
597,858
543,000
575,000
590,000
608,000
600,200
1,789,974
1,662,666
1,794,000
1,840,800
1,814,400
2,088,540
483,293,000
631,813,000
1,058,460,000
1,067,664,000
725,760,000
1,420,207,000
92,016
292,876
481,482
867,591
605,342
1,034,816
5
18
27
47
33
50
Source: IBGE (2012), MDIC (2012).
About 50 % of which (1.03 mmt) is exported. This volume represents three percent of the national soybean exports (32.99 mmt). 77 % of Sorriso's soybean exports in 2011 were shipped to China. This share had increased within only five years from 0.01 mmt in 2006 (14 % of Sorriso's soybean exports) to 0.80 mmt in 2011 (MDIC, 2012) (see figure 2.6).
Figure 2.6:
Sorriso - soybean exports (2006-2011)
t
1 200 000
1 000 000
800 000
Total exports
Exports to China
600 000
400 000
200 000
0
2006
2007
2008
2009
2010
2011
Source: MDIC (2012).
To perform the typical crop rotational system soybeans are produced as summer crop and corn
safrinha8 is harvested as winter crop. The area planted with soybeans slightly decreased from
608,000 ha (2010) to 600,200 ha (2011), which KLASENER (2012) justified with a loss of area to corn
as summer crop due to a favorable development of world market prices. Nevertheless, medium
productivity increased from 2.98 t/ha (2010) to 3.48 t/ha (2011)9, which notably outlines the national average productivity of 3.12 t/ha (IBGE, 2012).
8
The cultivation of the second culture is called corn safrinha, the 'small harvest' (PARKHOMENKO, 2004, p. 150).
9
Contributing to this gain might have been a favorable climate as productivity in adjacent production regions also increased, e.g., by 8% in Lucas do Rio Verde-MT. Even though, the increase in Sorriso is strikingly higher by 17%.
Chapter 2
2.2.2
15
Rondonópolis-MT
Rondonópolis encompasses a total area of 4,159 km² with a population density of
47.00 people/km² (IBGE, 2010a). The county is situated in southeast Mato Grosso at the traffic
hub, where the two principal soybean export highways BR-163 and BR-364 meet. With all truck
transports, which are driving up or down the BR-163 and BR-364, going past Rondonópolis, the
city is strategically situated on the Sorriso-Santos corridor.
Agricultural production contributes only a minor share of six percent to the GDP (192 million R$),
while 70 % of the GDP are generated by the service sector (2009: 2,345 million R$). The local industries benefit from the inputs carried from the southwestern seaports and agricultural products from Mato Grosso's farms. Plenty of haulers and transportation companies that ship agricultural goods to the intermodal terminals of Alto Araguaia-MT (rail terminal) or São Simão-GO (river port) as well as to the seaports and return with agricultural inputs are settled in the city (ANTUNES, 2012; ANONYMOUS 4, 2012). The trade balance of Rondonópolis reveals a high volume of
imports (2011: 2.16 mmt). 98 % of which (2.11 mmt) are agricultural inputs like fertilizer, pesticides and herbicides (COMEX/MDIC, 2011, Rondonópolis PPEXP).
In 2011, soybeans were planted on an area of 73,000 ha. The total yield comprised a volume of
227,760 t (IBGE, 2012). As a large volume of soybeans was purchased from other regions like Sorriso region (OSAKI, 2012), soybean export volumes exceeded production quantities in the past
(see table 2.3).
Table 2.3:
Rondonópolis - soybean production and exports (2006-2011)
Rondonópolis - MT
Year
2006
2007
2008
2009
2010
2011
Area
Quantity
Value
Exports
Export share
ha
t
R$
t
%
69,000
59,000
63,000
72,000
75,000
73,000
173,880
182,900
195,300
223,200
225,000
227,760
50,425,000
72,246,000
105,657,000
133,920,000
137,250,000
148,044,000
1,993,488
1,681,424
579,442
159,317
251,799
129,757
1,146
919
297
71
112
57
A big part of the volumes of soybeans produced or purchased is processed in big soybean crushing plants of trading companies. The processing capacities increased in recent years, which might
explain the decline of the soybean export volume with an increase in export products of processed soybeans (see figure 2.7). The still ongoing construction of a new rail terminal in Rondonópolis promises a further growth of trade by simplifying the access to the southern seaports
(ANONYMOUS 3, 2012).
16
Chapter 2
Figure 2.7:
Rondonópolis - Exports by product (2007-2010)
Year
2007
Cake and other solid
residuals from sybean
extraction
2008
Soybeans
Soybean oil products
Cotton products
2009
Other products
2010
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Source: IBGE (2010a).
2.2.3
Rio Verde-GO
Rio Verde comprises an area of 8.380 km2 with a population density of 21.05 people/km². It is
located in centersouth Goiás and belonged in 2011 to the eleven municipalities, which contributed most to the value of national agricultural production (916,419,000 R$; c.f. Brazil:
195,623,606,000 R$). Rio Verde has the largest agricultural production area (440,955 ha) of Brazil, 60 % of which (265,000 ha) were in 2011 used for soybean cultivation (IBGE, 2012; IBGE 2011,
p. 23). The principal agricultural products cultivated are soy and corn (PREFECTURE OF RIO VERDE-GO,
2010), which together constitute the typical crop rotation of the region with soybeans planted as
summer crop and corn as winter crop (SILVA M., 2012, OSAKI, 2012).
Corn production experienced a strong increase in recent years (see figure 2.8) that might have
been due to favorable climatic conditions as well as favorable world market prices, spurring the
expansion of corn cultivation (GLOBO NOTÍCIAS, 2012; KLASENER, 2012). With a produced quantity of
1.27 mmt of both corn and soy, Rio Verde participated with one percent in the 2011 national
corn and soy production (1.49 mmt) and is the largest grain and legume producer of Goiás (IBGE, 2012). Products from soybeans and corn, either raw or processed, hold a share of 98 % in the
total municipal exports (2011: 731 mmt), where raw soybeans represented a share of 36 %.
(COMEX/MDIC, 2011, Rio Verde PPEXP).
Chapter 2
Figure 2.8:
10³ t
900
800
700
600
500
400
300
200
100
0
17
Rio Verde - corn and soybean production (1990-2010)
Corn
Soy bean
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Source: IBGE (n.d.).
As plenty of industry is settled in the region, the major share of the locally produced soybeans
are traded on the domestic market (see table 2.4) and processed in the local factories
(ANONYMOUS 3, 2012). Even though, Rio Verde accounts for one percent of Brazil's exports of raw
soybeans (MDIC, 2012). In 2011, a soybean volume of 826,800 t valued with 523.36 million R$
was produced, representing one percent of the national soybean production. While planted area
remained constant, production volume increased by eight percent compared to the previous year
(2010: 768,500 t) and medium productivity leveled the national average of 3.12 t/ha (IBGE,
2012).
Table 2.4:
Rio Verde - soybean production and exports (2006-2011)
Rio Verde - GO
Year
2006
2007
2008
2009
2010
2011
Area
Quantity
Value
Exports
Export share
ha
t
R$
t
%
250,000
230,000
235,000
245,000
265,000
265,000
600,000
579,600
733,200
735,000
768,500
826,800
50,497
27,972
13,884
181,167
164,494
370,163
8
5
2
25
21
45
199,800,000
250,967,000
403,260,000
514,500,000
384,250,000
523,364,000
2.2.4
Barretos-SP
Barretos comprises an area of 1,566 km2 with 71.58 people/km² (IBGE, 2010a). It is located in
close proximity to the border to Minas Gerais state in the North of São Paulo, which is the state
that contributed most to the generated value of agricultural production in 2011 (SP: 18 %, cf.
18
Chapter 2
MT: 11 %) (IBGE, 2011, p. 21). However, São Paulo participated slightly with two percent in the
2011 national soybean production (71.82 mmt). With the yield of 1.27 mmt, which was generated on 488,342 ha, its productivity of 2.60 t/ha was below national average (3.11 t/ha) (IBGE,
2012).
Barretos can be described as a marginal area for soybean production, where the oilseed competes with sugarcane and dairy. In 2011, soybeans were cultivated on an area of 6,000 ha (8 % of
the total agricultural production area of 73,272 ha) and yielded a production volume of 14,400 t,
which represented a below-average productivity of 2.40 t/ha (IBGE, 2012). The legume is commonly used in the crop rotation with the perennial sugarcane culture (DIAS, 2012).
As like in São Paulo state10, the area planted with soybeans decreased in Barretos since 2005 (see
figure 2.9). In the mean time, the planted area with crops that have a high production value,
most notably sugarcane, was extended (CONAB, 2012). Nevertheless, Barretos is one of the major
soybean production regions of São Paulo state (FAESP, 2011).
Figure 2.9:
Area harvested in the county of Barretos (%)
100%
90%
80%
Sugarcane
Soybean
70%
60%
50%
40%
30%
20%
10%
0%
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Source: IBGE (n.d.).
Even though, soybeans are of marginal relevance for Barretos' exports, which are dominated by
meat products (90 % of local exports). In 2011, a small quantity of 1,723 t of soybeans was exported (see table 2.5), which represented two percent of Barretos' total exports (91.697 t), with
all of it leaving the country via Santos (COMEX/MDIC, 2011, Barretos PPEXP).
10
75% (5.22 mha) of the agricultural area of the state (6.96 mha) was planted with sugarcane (yield: 427,36 mmt). Soybeans were cultivated on 488,342 ha, which represented only 7% of São Paulo’s agricultural area, with a total production quantity of 1.27 mmt (IBGE, 2012).
Chapter 2
Table 2.5:
19
Barretos - soybean production and exports (2006-2011)
Barretos - SP
Year
2006
2007
2008
2009
2010
2011
Area
Quantity
Value
Exports
Export share
ha
t
R$
t
%
n/d
n/d
n/d
n/d
n/d
1,723
12
17,530
7,330
12,500
7,000
6,000
6,000
42,540
19,791
33,750
17,640
16,200
14,400
17,654,000
9,302,000
24,580,000
10,584,000
10,740,000
10,224,000
2.2.5
Santos port
Seaports play a critical role that play in the country’s access to the international market. About
90 % (700 mmt) of Brazil's total exports of goods is shipped to international destinations via the
seaports (SEP, 2012). The principal seaports for soybean exports, which handle 81 % (26.97 mmt)
of the soybean exports, are situated in the South and Southeast (see appendix 12).
Santos is the principal trading port for Brazilian soybeans, where 28 % of the Brazilian soybean
exports are operated (2011: 9.23 mmt). The total volume of goods exported in 2011 via the port
was 62.88 mmt (CODESP, 2011, p. 43). According to the WORLD BANK (2010), more than 70 % of
the total volume traded at Santos are agricultural and food products. The main commodities
traded include sugar (2011: 16.93 mmt, 27 % of total exports), soybeans in raw or crushed form
(9.77 mmt, 16%) and corn (4.57 mmt, 7 %) (CODESP, 2011, p. 48, 49). About 65 % of the soybean
volume exported via Santos is transshipped between January and May (2010: 5.49 mmt; 2011:
5.29 mmt), corresponding with the productive months of the harvesting season (see chapter
4.1.3 and appendix 13). 80 % (7.43 mmt) of the volume was in 2011 directed to China (MDIC,
2012). The seaport is particularly for soybeans originated in the Sorriso-Santos corridor an important transshipment point (see figure 2.5). Here, 83 % (0.86 mmt) of Sorriso's total exports
(1.03 mmt) and 97 % of the exports of soybeans originated in Rio Verde are handled.
Table 2.6 represents the volumes of the selected production regions exported via Santos port.
Soybeans from Rondonópolis are distributed to all of the southern ports. Barretos that plays a
marginal role as a soybean producer and exporter is in terms of soybean export volumes of no
relevance for Santos, although it exports only via Santos (MDIC, 2012).
20
Chapter 2
Table 2.6:
Santos - soybean export volumes of selected regions (mmt)
2011
2010
2009
2008
2007
2006
mmt
Sorriso-MT
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
0.86
0.06
0.37
0.00
0.50
0.11
0.16
0.00
0.59
0.05
0.18
0.00
0.38
0.18
0.01
0.00
0.07
0.41
0.03
0.00
0.01
0.64
0.05
0.00
Total
9.23
8.23
8.67
7.16
4.53
6.96
Centrally located at the coast of the state São Paulo, the port extends along an estuary from the
Atlantic Ocean (CODESP, 2012). FIESP (2012) reported a width of 100.00 m and a maximum depth
of 13.50 m so that even big vessels like Panamax bulkers that draw up to 12.04 m of water
(PANAMA CANAL AUTHORITY, 2005) are able to dock at Santos port. The WORLD BANK (2010,
p. 43), however, revealed that the operational draught is limited to 11.80 m and judged the access by water as unsatisfactory.
Access by land was judged as critical. Despite offering extensive road and rail networks, São Paulo
State and, hence, Santos lack basic intermodal integration and suffer from continued bias towards road-based transport (USITC, 2012, p. 3-17). Figure 2.10 illustrates Santos' connection to
the national road and rail system. The railways of MRS Logística and AMÉRICA LATINA LOGÍSTICA [ALL]
are terminating at the port. However, a study performed by the WORLD BANK (2010) found that
57 % of grain cargo and food products is being carried to Santos port by trucks. The limited inland
port access and insufficient reception capacity of trucks, especially during peak harvest periods,
lead to delays and demurrage costs for ships waiting to be loaded as well as for trucks waiting to
be unloaded, respectively (USITC, 2012, p. 3-17).
Figure 2.10:
Connection of Santos port to the national transport system
BR 101
SP055
SP160
SP150
BR 101
SP150
Federal road
Two-lane federal road
Maritime
port
Rail track
Source: MINISTRY OF TRANSPORT (2012). Modified by author.
Figure 1. Connection of Santos port to the national
transport system.
Source: MINISTRY OF TRANSPORT (2012). Modified by author.
Chapter 2
21
Santos port, which is operating 24 hours during seven days per week, is administered by the federal dock company COMPANHIA DOCAS DO ESTADO DE SÃO PAULO (CODESP, 2012). Despite the public
ownership, most public ports are run by private terminals. Exports of soybeans are at Santos only
handled at private terminals. Since 1995 the port authorities issued concessions of 20 years or
more to port operators who handle the port operations and take care of the port's infrastructure.
The system is a result of a government regulatory reform and privatization efforts in the 1990s
(see appendix 14) that aimed to generate private sector investments in infrastructure like the
construction of warehouses (ANONYMOUS 2, 2012; USITC, 2012, pp. 3-14). For the time being,
45 warehouses that are located within the port area, and 39 external warehouses offer a total
storing capacity of 416,395 t (FIESP, 2012). But, according to ANONYMOUS 2 (2012) and USITC
(2012, p. 3-18), investments are being discouraged by the time limit of the concession. Hence,
outdated equipment, labor intensive port processes and inadequate port administration are generating inefficiencies (EIU, 2010, p. 11).
2.3
Characteristics of soybean logistics and transportation in Brazil
The COUNCIL OF SUPPLY CHAIN MANAGEMENT PROFESSIONALS (2012) defined logistics as "process of
planning, implementing, and controlling procedures for the efficient and effective transportation
and storage of goods including services, and related information from the point of origin to the
point of consumption for the purpose of conforming to customer requirements". To ensure efficient and effective transportation and storage the logistics process needs to be well managed.
The USITC (2012, p. 3-1) judged transportation infrastructure as one of the factors that most
broadly affect Brazilian agricultural competitiveness. A developed transportation infrastructure
can benefit the industries to competitively produce, move, store and market the goods in domestic and export markets. A poor transportation infrastructure, on the other hand, may impact on
delivery cost and reliability of supply.
The five principal modes of transportation are road, rail and water transport as well as aviation
and pipeline (MANGAN/LALWANI/BUTCHER, 2008, p. 5). Distance and time of delivery are directly
influencing transportation costs and reliability of supply. Therefore, driven speed as well as the
geographical locations of the transport's origin and domestic destination are key factors for the
selection of the transport mode. In order to ensure an efficient logistic process without breakdowns and minimize costs, the choice is determined by the transport medium's costs, speed,
flexibility, local access and available capacities. The primary restriction is the existing infrastructure network in the region of activity.
Soybeans in granular form are handled as bulk cargo, which is generally transported in large volumes and shipped via road, railroad or waterway. Transportation by air and pipeline is not
relevant for soybean shipping. Due to high flexibility, frequency and speed, trucking is advantageous for short-distance transports of soybeans (see table 2.7 and appendix 15). But soybeans
22
Chapter 2
and its domestic movements are characterized by seasonality, long distances, great quantities
and low quotients of value to freight. While trains and barge convoys can intake volumes of minimum 6,000 t (GARCIA, 2012; SANTOS/CARDOSO/MOITA, 2012, p. 69), the biggest Brazilian truck model for soybean transports carries 57 t net weight (ANTUNES, 2012). LORETI (2011, pp. 23) considered
accordingly rail and waterways as the most suitable options for the transportation of agricultural
products and soybeans in Brazil. Both modalities offer efficient shipping of large quantities over
long distances at low costs per unit (USDA, 2010, pp. 200) and, due to their lower fuel consumption compared to a truck11, lower GHG emissions per tkm (see chapter 2.3.6) (APROSOJA, 2011;
MANGAN/LALWANI/BUTCHER, 2008, p. 135). Based on data from 2002 to 2004 CAIXETA-FILHO (2006,
p. 2) compared average freight costs for soybean transportation in Brazil and confirmed that waterway transportation is the cheapest and road transportation the most expensive mode. He revealed that road freight costs twice as much as waterway freight (see appendix 16). For 2011,
MONTEIRO/SEBBEN/GOLIN (2011, p. 3) analyzed that transportation of one ton of cargo via road is
about 3.8 times more expensive than transporting the same amount via waterway.12
Table 2.7:
Comparison of transport modalities relevant for soybean shipping
Fixed costs
Variable costs
Capacities
Speed
Flexibility
Frequency
Suitable distance
Road
low
high
low
high
high
high
short
Railway
high
low
high
medium
medium
medium
long
medium
low
high
low
small
low
long
Waterway
Source: Loreti (2011). Modified by author.
The disposability of trucks in even distant rural areas and its flexibility with respect to route planning and loading or unloading stations is particularly important for the Brazilian agribusiness as
the major soybean production regions are distant to the coastal ports without necessarily having
direct access to waterways or railways (CAIXETA-FILHO, 2006, p. 2). Inferentially, road transportation is obligatory for transporting soybeans from farm to warehouse and point of transshipment.
Figure 2.11 demonstrates the dependence of the Brazilian soybean transportation matrix on road
transportation. Whereas in the USA 61 % of soybeans are shipped via the interior waterway system and 16 % by truck, it is a share of 60 % of Brazilian soybeans (approximately 45.20 mmt) that
is moved via the national road network.
11
According to AFONSO (2006, p. 89) a barge travels a distance of 219 km with one liter of fuel and one ton cargo while a
train travels 86 km and a truck 25 km.
12
Costs per tkm reported by MONTEIRO/SEBBEN/GOLIN (2011, p. 2): 17 US$/tkm (waterway), 65 US$/tkm (road).
Chapter 2
Figure 2.11:
Transport matrix for soybean shipping
100%
90%
23
2%
7%
Waterway
16%
Railway
80%
33%
70%
61%
Road
60%
50%
40%
82%
30%
60%
23%
20%
10%
0%
16%
Argentina
Brazil
USA
Source: Created by author. Based on ABAG (2005).
NUNES (2010, pp. 53) and CAIXETA-FILHO (2003, p. 2) explained the predominance of the road mode
by the larger extension of installed highways13, due to concentrated governmental investments in
the road system during the 1950s and 1960s, as well as by difficulties faced by the other
transport modalities to supply the mostly remote production regions. LÍCIO (1995) outlined the
relevance of access to infrastructure and the integration of transportation corridors for intermodal transportation in order to improve competitiveness of the agricultural products and interconnect production areas with the national and international markets. Accordingly, CAIXETA-FILHO
(2003, p. 2) reasoned that the use of the rail and waterway modes, either unimodal or combined
with the road mode, reflects competitiveness-related advantages.
2.3.1
Road transportation
The Brazilian road network links producers, manufacturers and service industries to grain elevators, processing plants, markets and ports. Agricultural inputs like fuels and fertilizers are carried
to the production sites and agricultural goods shipped to the domestic destination via the roads.
However, the quality of the road networks is of great disparity. Despite constituting 60 % of the
needs for soybean transports (see figure 2.11), only fourteen percent of the total 1.58 million km
of roads and only two percent of the roads under municipal administration are paved (see table 2.8).
13
Road network: 1,583,331 km; railroad network: 30,051 km; navigable waterways: 29,000 km (CNT, 2012c).
24
Chapter 2
Table 2.8:
Brazilian road system extension (km)
Paved
Non-paved
Total
Federal
State
Municipal
km
km
km
64,165
110,842
26,827
12,817
111,334
1,234,918
76,982
222,176
1,261,745
Total
km
219,089
1,364,242
1,583,331
Share
%
13.8
86.2
100.0
Source: Created by author. Based on data of CNT (2012c).
According to a recent survey (CNT, 2012a), 63 % of the paved Brazilian roads (60,053 km) are in
regular, bad or poor shape (see appendix 17). Lacking maintenance of the surface reportedly
generates infrastructural deficiencies that directly influence operational costs of vehicles (see
figure 2.12) and may lead to a lengthening of the travel time (CNT, 2011, p. 25). Critical points are
holes in the pavement, erosion of track, damaged bridges and obstacles on the street (CNT,
2012a, p. 6). CNT (2011, p. 285) underlined the corresponding effect of increased exhaust emissions. In order to increase private investments the government issued in the 1990s concessions
to private entities for operating roads (FLASKERUD, 2003, p. 5; USITC, 2012, p. 3-8). 87 % of the
tracks under private administration were rated in the 2012 by CNT as good or very good (CNT,
2012, pp. 78). But these are concentrated in the state of São Paulo and Rio de Janeiro whereas
long-distance transports from the Center West are principally travelling on public streets. Most of
the federal roads within the Sorriso-Santos export corridor are in regular or good conditions,
while state administered roads are mainly rated as bad or poor by CNT (2012, pp. 250).
Figure 2.12:
Impact of road condition on operational costs
Very good
Good
Regular
Bad
Poor
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Source: CNT (2011, p. 277).
Since running an own truck fleet incurs high fixed costs, the majority of the soybean marketers
takes advantage from contracting transport service providers. These include haulers with own
fleet, self-employed drivers with own trucks and transport agencies without fleet. Fleet owners
are equipped with a determined number of trucks, offering their logistics services. They are contracted directly either by grain sellers and traders or by transport agencies. Transport agencies
act as intermediaries who subcontract third parties. They are contracted by the grain seller or
Chapter 2
25
trader to organize the required capacities of trucks and manpower. Estimated 50 % of all
transport services are provided by self-employed drivers (NUNES, 2010, p. 58; IPEA, 2011, p. 127)
who are also contracted by freight forwarding companies. Those possess an own fleet but often
need to enlarge their capacities for accomplishing individual contracts. Big trading companies
with an own truck fleet are rare and are generally organizing the transportation in a subsidiary of
the company, which might also offer its services on the market.
Transport companies aim to use the vehicle at maximum capacity so that backhauling trucks are
in most cases loaded with other products than soybeans (PARREIRA, 2012). The Brazilian fleet consists of 2.33 million trucks (CNT, 2012c) with 32 % of it being heavy-duty trucks (MMA, 2011,
p. 27). It is unevenly distributed all over Brazil with a concentration in the South and Southeast.
While Mato Grosso is the largest soybean-producing state, only three percent of the national
fleet is registered here (USITC, 2012, p. 3-6). During peak harvest season the insufficient local
trucking capacities is revealed. The excessive demand has thus to be satisfied by trucks sent from
other states. The USITC (2012, p. 3-6) declared this as a driving factor of freight prices and a reason for congestion on deficient roads within the export corridors.
2.3.2
Rail transportation
The Brazilian rail network extends 30,051 km14 (CNT, 2012c), on which in 2011 a volume of
470 mmt was transported (CNT, 2011a, p. 22). Approximately 50 % of the rail network is concentrated in the South and Southeast of Brazil (see appendix 18), where areas are rich in mineral
deposits and agricultural production is strong. The principal goods transported are iron ore and
coal (2011: 77 %; 364 mmt) as well as agricultural products (12 %; 55 mmt). Soybeans cover five
percent of the total cargo transported on Brazil's railroads with the major part being originated in
Mato Grosso (ANTF, 2012). The Brazilian rolling stock, comprising the equipment to form rail
compositions, totaled 101,983 cargo wagons and 3,093 train engines in 2011 (ANTT, 2012a, p. 8).
In 1957 the national rail system still extended 37,000 km. A market regulating policy as from the
1960s inhibited private participation while governmental investments were mainly directed to
the developing automotive industry and the national road network (ANTT, n.d.). Maintenance of
the railroads was widely neglected so that the network deteriorated and diminished in extension
(RODRIGUES, 2005 cited by SANTOS, 2007, p. 22). Investments recovered when the state railroad
companies were privatized. From 1996 to 1998 the government issued concessions for a period
of 30 years in order to spur the productivity of the sector (CASTILLO/VENCOVSKY/BRAGA, 2011, pp.
20; USITC, 2012, p. 3-9). The long-term agreements made investments attractive as the conces-
14
According to USITC (2012), this represents one seventh the size of the rail network in the USA which extends
225,000 km. Other countries with similar dimensions are Russia with a rail network of 87,000 km, China with a rail network of 86,000 km and India with a rail network of 64,000 km.
26
Chapter 2
sionaires can benefit from the return on profitable investments. As a result, private concessionaires (see appendix 19) currently operate 95 % of the national rail system (28,614 km).
The development of the rail system in the early 20th century was due to the expansion of the
Brazilian agribusiness supported by a high participation of private investors. While the individual
regional rail networks were extended, they were not linked at a supraregional level. This led to
the problem that until today track gauges differ from region to region, which creates difficulties
in further integrating the system. Prevailing types of gauge are the metric gauge (1 m), the broad
gauge (1.6 m) and mixed gauges (1.0 m to 1.6 m). According to ANTT (2012a, p. 4) the metric
gauge accounts for nearly 80 % of the total Brazilian rail kilometers, and predominates in the
southern region. The broad gauge accounts for 19 % of total railroads and prevails in the Southeast and Center West region, leaving about two percent mixed. New rail tracks shall use a broad
gauge of 1.6 m as it provides better stability to trains (USDA, 2011). The deficient connections of
the networks under concession and their different track gauges inhibit the employment of
equipment in different regions for maximizing the loading factor and limit the competitiveness of
the transportation modality (USITC, 2012, p. 3-9). According to GREGOIRE (2011, p. 7) only ten percent of the rail network is fully utilized. These inefficiencies influence the competitiveness of the
soybean business, e.g., by elevated freight rates.
Another effect of this concession model was the emergence of local monopolistic market structures in the rail transport sector as only one concessionaire was appointed per track. The limited
competition led to an untransparent monopoly pricing system and increased freight prices, getting close to the price level of truck transport (see also chapter 4.2.1). MONTEIRO/SEBBEN/GOLIN
(2011) compared rail freight prices of Brazil and the USA and revealed that rail transport is in Brazil almost three times as expensive per tkm as in the USA over similar distances.15
The Center West region is covered by the rail networks ALL - MALHA NORTE [ALLMN] and ALL MALHA PAULISTA [ALLMP], which together constitute the Santos corridor (CNT, 2011a, p. 79). Both
are run by the private company ALL and have the broad gauge (1.6 m), which enables trains to
travel from Mato Grosso directly to Santos port (ANTT, 2012a, pp. 4). The ALLMN railroad extends 512 km from Alto Araguaia-MT to Aparecida do Taboado-MS, where it connects to the
ALLMP railroad that ends in Santos (ANTT, 2010, p. 11). 250 km of railroad, linking Alto Araguaia
and Rondonópolis, are currently under construction (VENCOVSKY, 2011, p. 52).
Soybeans (2010: 3.65 mmt), soy meal (1.82 mmt) and corn (4.84 mmt) account for 98 % of the
total volume transported (10.50 mmt) on the ALLMN railroad in direction Santos (ANTT, 2010,
appendix). Between 50 % (SILVA, 2010, p. 8) and 80 % (ALL, n.d.a) of the soybean exports from
Mato Grosso are transported via this railroad. The area of influence extends principally over
southern and central Mato Grosso, including Sorriso, northeastern Mato Grosso do Sul and
15
Road: 0,051 US$/t/km; Rail: 0,0186 US$/t/km (MONTEIRO/SEBBEN/GOLIN (2011).
Chapter 2
27
southwestern Goiás, including Rio Verde. The most important intermodal terminal for soybean
and corn transshipments is located in Alto Araguaia where about 10 mmt of grains, oilseeds and
byproducts are turned over per year (ALL, n.d.a). Returning trains carry principally fertilizers to
the Center West (CNT, 2011a, p. 30) but most of the wagons return empty (about 70 %) (GARCIA,
2012). That way, being linked to the rail network of São Paulo state, the ALLMN railroad connects
major soybean production regions to the port of Santos.
2.3.3
Even though, as pointed out before, waterway transportation has the lowest costs for bulk transports over large distances the use of this modality is still modest in Brazil (see figure 2.11). The
country has a waterway system of 44,000 km and 29,000 km of navigable rivers, of which only
13,000 km (45 %) are used for commercial navigation (CNT, 2012c). In 2011, a total volume of
25.14 mmt has been moved on the inland waterways.
The primary commodity moved on the waterway is iron ore, accounting for 21 % of the total cargo transported (5.32 mmt). Soybeans are the second largest commodity group with 17 %
(4.24 mmt) (ANTAQ, 2012a, pp. 26-30). Figure 2.13 illustrates the characteristic of the seasonal
output of transportation services. This corresponds with the period of soybean exports from February to September (see chapter 4.1.3). An advantage, as the months of high demand for waterway transports (February-April) coincide with the rainy season whereas a low water level during
dry season including August to December can paralyze transportation activities like in 2010
(ANTAQ, 2011, p. 5).
Figure 2.13:
Thousand tons
900
800
700
600
500
400
300
200
100
0
Jan
Demand for soybean transportation via the waterway (2010-2011)
2010
Feb
Mar
Apr
May
Jun
Jul
2011
Aug
Sep
Oct
Nov
Dec
Source: ANTAQ (2012a).
The NATIONAL AGENCY FOR WATERWAY TRANSPORTATION [ANTAQ] divided Brazil into 12 hydrographical
regions, which are illustrated in appendix 20. Only five areas are relevant for cargo transportation, namely the Amazonas region, Tocantins/Araguaia region, Paraná region, Paraguay region
and Atlântico Sul region. SANTOS/CARDOSO/MOITA (2012, p. 67) asserted that 90 % of the trans-
28
Chapter 2
ported volume is concentrated in the northern region, particularly in the Amazonas region including the waterways Solimões-Amazonas, Madeira and Tapajos.
The waterways of relevance for the selected regions (see chapter 2.2) are the Madeira, Tapajós
and Paraná-Tietê waterways. Soybeans produced in western and northern Mato Grosso (2011:
9.67 mmt; MDIC, 2012) leave the state by truck mostly in direction Porto Velho-RO (access to
Madeira river) or Itaituba-PA (access to Tapajos river), where the bulk is transshipped to barges
and carried over a distance of approximately 1,675 km to the seaport of Santarém-PA (ANTAQ,
2011, p. 5). In 2011, 2.24 mmt of soybeans have been shipped on the Madeira waterway
(ANTAQ, 2012, p. 11), where barge convoys of approximately 24,000 t are moving. The Tapajos
river (extending 345 km) enables barge convoys with about 7,500 t loading capacities to travel to
Santarém. But due to lacking infrastructure in northern Mato Grosso access is limited, wherefore
it accounts for a small share of soybean exports yet (no data on volumes available) (RIBEIRO, n.d.;
ANTAQ, 2012, p. 16).
The PARANÁ-TIETÊ shipping corridor is the most developed waterway of the country in terms of
infrastructure and equipment (ANTAQ, 2012a, p. 23). It extends 1,653 km from São Simão-GO in
southeastern direction (ANTAQ, 2012a, p. 24) and transports soybeans from the regions of
southern Mato Grosso, northern Mato Grosso do Sul and south Goiás. Barge convoys with capacities up to 4,800 t (RIBEIRO, n.d.) carry the cargo from São Simão-GO to Pederneiras-SP (about
680 km), where exported soybeans are transshipped to trucks or train and transported to Santos.
In 2011, 1.17 mmt of soybeans and 0.48 mmt of corn have been shipped that way, accounting
together for 53 % of total volume transported (3.11 mmt) on the waterway (ANTAQ, 2012, p. 11).
Due to the large volumes that have to be transported in order to make transportation by barges a
profitable option, waterway transport is principally utilized by traders with large handling capacities, offering a low-cost transportation alternative for goods originated in production areas in the
proximity of the inland waterways. As Brazil's inland waterways, except of the Tietê-Paraná, do
not connect the remote soybean production areas with economic centers, intermodality including trucking is necessary to ship soybeans to the seaports (USITC, 2012, p. 3-14).
2.3.4
Storage
MATTHÄUS/MÜNCH (2009, p. 113) define storing as a way of keeping goods available over a period
of time in order to satisfy demand at a later date and maintaining the goods at a high quality level with minimal loss of substance. Soybeans are stored in silos or flat storage systems (MATTHÄUS/MÜNCH, 2009, p. 126). Elevators realize the transport between tipping gutter of the transshipment station, where the truck is loaded and unloaded, and storehouse. To achieve maximum
economic efficiency high intake and discharge capacities as well as low labor requirements are
necessary (MATTHÄUS/MÜNCH, 2009, p. 125). According to SEIBEL (2005, pp. 192), those should be
between 70 t/h and 120 t/h to avoid long waiting times during harvesting season.
Chapter 2
29
As oilseeds are living organisms whose chemical composition changes with their development
(PANIZZI/MANDARINO, 1994, p. 242), soybeans are sensitive to humidity and temperature during
storage (MATTHÄUS/MÜNCH, 2009, pp. 94, 104). Post harvest management helps to protect soybeans from deterioration and achieve stabilization on a high quality level with optimum moisture
level for handling (SEIBEL, 2005, pp. 228). The moisture level of the seed averages twelve to fourteen percent at harvesting (KLASENER, 2012) and is the major determinant on storability of soybeans (see appendix 21). If the moisture level exceeds thirteen percent drying is necessary to
reduce the risks of decreased seed quality and spoilage from, e.g., seed respiration and mold attack (ACASIO, n.d., p. 5). Cooling and ventilation can diminish or inhibit respiration and metabolic
processes. These are stimulated by warehouse temperatures higher than the optimum and favor
the development of microorganisms like fungi and insects (MATTHÄUS/MÜNCH, 2009, p. 114). The
release of toxic gases in small doses into the air is an additional measure to prevent pest infestation (GARCIA, 2012). That way, losses can minimized and the availability of good quality products
when needed for marketing at a later point of time ensured (ACASIO, n.d., p. 5). The post harvest
management enables to meet the values of the export standard represented in appendix 22,
which have to be met for liberating the product for export.
The Brazilian warehousing capacities for grains and oilseeds did not keep pace with the rapid
growth of the agricultural production. The country currently suffers a shortage of storage
capacities for agricultural bulk goods. For the time being a total volume of 143.22 mmt of static
storage capacities is available, 118.22 mmt of which are covered by warehouses specialized on
bulk goods (CONAB, 2012b). These warehouses only have the ability to gather 89 % (118.22 mmt)
of the volume of the major agricultural bulk goods produced in 2011 (160.17 mmt) (see appendix 23) (IBGE, 2012). Considering that international recommendations suggest a warehousing
capacity of at least 1.2 times of the average production volume (GALLARDO et al., n.d., p. 2) the
need for further investments is clearly reflected. Distribution difficulties, which arise due to an
uneven geographical scattering of warehouses within the country (see figure 2.14), and local deficits in storage capacities make matters even worse (USITC, 2012, p. 3-14).
Figure 2.14:
mmt
80
70
60
50
40
30
20
10
0
Storage capacities vs. production per macro region (2011/12)
-24%
67.2
51.3
Production volume of major grains and oilseeds
Storing capacities for bulk goods
-22%
57.3
44.7
15.8
13.4 -16%
15.2
6.8 -55%
4.4
South
Center West
South East
North East
Source: Created by author. Based on data from CONAB (2012) and IBGE (2012), listed in appendix 23.
2.1 -52%
North
30
Chapter 2
On-farm storing is in Brazil not very common yet. According to CONAB (2011), only 14 % of rural
producers in Brazil16 have warehouses located on their farms (see appendix 24). The major part of
the producers depends on the off-farm storage infrastructure. With the principal share of offfarm warehouses being situated in urban areas (44 %), traffic volume of trucks is locally immense
during harvest. 36 % of the warehouse capacities are located in rural areas whereas the Brazilian
ports account for six percent of the total storage capacities (MORCELI, 2012).
Warehouses are important to ensure a smooth flow of the supply chain by preventing distribution problems through congestion at transshipment stations and ports during harvest season
(GALLARDO ET AL., n.d.). Scarce capacities impede the producer to benefit from higher market prices
or lower freight rates at a later point of time. For a limited time seasonality of the crops can hold
the balance between supply and demand. But with the beginning of the next harvest, producers
are forced to take out the stored soybeans in order to take in the freshly harvested crop (FERREIRA, 2012). Hence, the limited storage capacities lead during harvesting time to a high supply of
soybeans and a great demand for transportation services. As a result the commodity price tends
to lower while the freight price increases in comparison to the interim periods, impacting negatively on local producer prices (KUSSANO/BATALHA, 2012, p. 625).
2.3.5
Policy and investments
A lack of incentive policies and investments in infrastructure during the last decades had led to
underdeveloped and generally sub-standard waterways and overland rail transportation infrastructure (HUERTA/MARTINS, 2002, p. 4). Over the past 40 years the percentage of GDP for infrastructure investments has been declining from averaged five percent in the 1970s to two percent
in the 1990s and 2000s (see appendix 25) (MORGAN STANLEY, 2010, p. 3).
This trend was reversed only with the implementation of the governmental GROWTH ACCELERATION
PLAN [PAC] (2007-2011). When exports increased during the 1990s and 2000s, it became apparent that the country's infrastructure was not capable to satisfy the additional demand for transportation and logistics services (FILARDO ET AL., 2006, p. 37) and the government recognized the
need for investments in infrastructure. In 2007, it launched the PAC program with the goal to
change the general cargo transportation matrix, by increasing the participation of rail and water
transportation, and develop an integrated intermodal system (see figure 2.15). The PAC is part of
the NATIONAL PLAN OF LOGISTIC AND TRANSPORTATION [PNLT], which provides funds in three phases
from 2008 to 2023 for strategic investments in infrastructure with the focus set on rail and port
infrastructure (see appendix 26). The infrastructure investment rate leveled in 2011 36 % of the
GDP (CNT, 2012, p. 328).
16
Share of on-farm storing: USA 65%, European Union 50% (MORCELI, 2012).
Chapter 2
Figure 2.15:
31
Transport matrix - 2005 and 2025 (%)
70%
Road
Rail
Water
Pipe
Air
60%
58
50%
-48%
40%
30%
35
+40%
30
20%
10%
25
29
+123%
13
4
0%
2005
0
5
1
2025
Source: Created by author. Based on Ministry of Transport/Ministry of Defense (2007).
2.3.6
Climatic impact of soybean transportation
According to the IPCC (2007a, p. 330) and ECMT (2007, pp. 21), the transportation sector including transportation of cargo and persons is a considerable continuously growing source of greenhouse gas emissions. Its contribution to overall emissions is thirteen percent (see appendix 27),
with the majority of the transported cargo being industrial goods (EPA, 2012). However, the increasing awareness of policy makers and consumers of the climatic impact of purchased goods in
recent years made it relevant to marketers to analyze and optimize the global warming potential
of the respective products (ECMT, 2007, pp. 17).
Greenhouse gases trap heat and absorb infrared radiation in the atmosphere. This leads to the
effect of global warming because the average temperature gradually rises in correlation with the
increase in concentration of the gases (EPA, 2012). GHGs include carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O) and fluorinated gases (see table 2.9), of which CO2 accounts for the
largest share of emissions (82 % in 2008; UNFCC, 2010, p. 10). Burning of fossil fuels, which are
major inputs for powering engines of transportation vehicles, is the principal reason for CO2
emissions (EPA, 2012).
32
Chapter 2
Table 2.9:
GHG
CO2
(Carbon dioxide)
CH4
(Methane)
N 2O
(Nitrous oxide)
Greenhouse gases and sources of exhaust emissions
GWP
1
21
310
Principal emitter
(economic sector)
Sources of emission
Transportation
Burning fossil fuels (coal, natural gas and oil),
solid waste, trees and wood products
Industry and Transportation
Production and transport of coal, natural gas and
oil, livestock, other agricultural practices, decay of
organic waste
Agriculture
Agricultural practice, industrial activities, combustion
of fossil fuels and solid waste
Source: Created by author. Based on information of EPA (2012).
Freight transport occurs between nearly all succeeding process steps of a product system and is
necessary for delivering the product from producer to consumer. Hence, it is of major interest to
know the GWP of the transportation processes in order to be able to optimize the exhaust emissions of the soybean export process. The GWP is a relative measure of the total energy that a
given mass of GHG is estimated to contribute to global warming, compared to the same mass of
CO2 (EPA, 2012). The GWP is measured in CO2 equivalents (see table 2.9), which are based on the
calculations of IPCC (2007) over a time horizon of 100 years. Inferentially, the presented gases
directly influence the climate. The international KYOTO PROTOCOL of 1997 called for a 60 % reduction in total carbon emissions17 by 2050 (relative to the 1990 level), in order to stabilize the emission of GHGs in the atmosphere and curb global warming (ECMT, 2007, p. 20).
In Brazil, road transportation accounted in 2011 for 97 % (36.38 billion l) of the domestic diesel
oil consumption (37.70 billion l) and contributed nine percent (136.15 billion tCO2/year) to the
country's total GHG emissions (1,574.54 tCO2/year) (CNT, 2012e). According to CNT (2012, p.
358), the share of road transports of general cargo and simultaneously its emissions are steadily
increasing. The current fleet of Brazil consists of 2.33 million trucks (CNT, 2012c), 32 % of which
are older than 20 years and about 17 % are even older than 30 years (CNT, 2012d). According to
IPEA (2011, p. 126), more than 50 % of the truck fleet run on engines that predate the 'Euro 0'
phase (see appendix 28). Furthermore, approximately 63 % of the paved Brazilian roads are in
regular, bad or poor condition (see chapter 2.3.1). The characteristics of the truck fleet and deficient road qualities can lead to increased fuel consumption and result in increased exhaust emissions (IPEA, 2011, p. 127).
As described in chapter two, about 60 % of the domestic transports of Brazilian soybeans is realized with trucks. In the 2010/11 cropping season, a volume of 45.15 mmt, of which 33.79 mmt
17
UNFCC (2010) explained that the term carbon emissions includes total aggregate GHG emissions, which implies
that GHG emissions are calculated as a weighted sum of CO2, CH4, N2O and the fluorinated gases HFCs, PFCs and
SF6; the sum is made using the GWP.
Chapter 2
33
were destined for export, was transported by trucks. Due to their remote geographic location,
the Brazilian soybean exports from the major production regions in the Center West require a
high participation of road transportation over large distances (see chapter 2.3). SILVA ET AL. (2010)
concluded that improvements in the logistics of transportation, e.g., by relocation of transport
services from road to rail, can contribute to significantly reducing GHG emissions. Performing a
life cycle assessment [LCA] (see chapter 3.6) for soybeans produced in the Center West, they
were able to show that domestic road transport from remote production regions to the seaport
contributes even more (19 %) than ocean transport from Brazil to Europe (11 %) to the total GHG
emissions (see figure 2.16).18 The major factors of influence on global warming within the soybean life cycle revealed by SILVA et al. (2010) are deforestation (29 %) and crop production (38 %),
including the production process and inputs used, e.g., fertilizers (11 %) and diesel (11 %). The
study demonstrated that road transport and deforestation represent the major determinants for
differences in overall emissions for soybeans produced in south Brazil and the Center West.
There were no comparable studies in order to validate the results.
Figure 2.16:
Amount of kgCO2eq emitted during the life cycle of soybeans produced in the
Center West
kg CO2eq/ton
350
Transport ocean
Transport river
Transport rail
Transport road
Deforestation
Pesticides
Diesel
Machines
Fertilizers
300
250
200
150
100
50
0
Production
Deforestation
Transport
Source: SILVA ET AL. (2010). Modified by author.
In contrast to the South, deforestation is in the Center West still in progress (NIEDERMEIER, 2012).
This factor was neglected in the calculation in the LCA for soybeans from the southern region.
Furthermore, the distance travelled is much lower, which leads to lower emissions of road transportation. As a result, a noticeably larger part of emissions was accredited to the crop production
process (62 %) while road transportation accounted for only 12 %. For both cases, road transportation held a larger stake on total CO2 emissions than rail and water transportation modalities
that account only for a small share.
18
The presented values of the specific transport modalities are weighted means, which were calculated by assessing the
percentage of soybeans transported by road, rail and waterway 18 to the studied distances. Numbers of reference in SILVA et al. (2010): 58% soybeans transported by road, 25% by rail, 13% by waterway.
Chapter 3
3
Methodology
35
Methodology
For understanding the theoretical approach of the study, this chapter at first explains in brief the
own research approach for determining the relevance of domestic transportation and logistics
processes of soybean exports in terms of costs and CO2 emissions between farm gate and seaport for selected regions in Brazil. Subsequently the sources of data and information as well as
the selection of assumptions for the market and cost analysis are discussed.
3.1
Own research approach
The study was realized in three phases. The first phase included a thorough literature review and
the posing of the research objectives. In this period prior to the field research an interview guideline was created and the organizational framework for the research trip set up. In a second
phase, primary data was collected in the field in Brazil by interviewing researchers and experts,
which belonged to transportation and trading companies. Visits to the logistics facilities of the
ALLMN rail terminal in Alto Araguaia-MT and to the private port terminal of a trading company
(volume of soybeans traded ≥ 2 mmt/year) in Santos-SP as well as visits to two farms in the county Rio Verde-GO provided insights into the operational processes and enabled a panoramic view
over various stages of the process chain. In a third step data was worked up and a market and
cost analysis performed. The applied methods are explained in detail in the following subchapters.
3.2
Market analysis
The market analysis was based on the approach of AAKER (2005, pp. 78). The purpose of a market analysis is to determine the structure and attractiveness of a market as well as to understand
its processes and dynamics. As its scope depends on the available data, this study was limited to
the observable factors. These comprised trade flows, prices, infrastructural conditions and market trends. The analysis included structure and size of the market, distribution systems, cost
structure and market trends, using the examples of the case study regions (AAKER, 2005, pp. 79).
Information for the determination of the market size and on trade flows was sourced from government data and associations. The data on Brazilian supply and demand were achieved from
ABIOVE while for world market development of soybean production, consumption and trade, the
USDA database PSD ONLINE was consulted. The data refer to the individual local marketing years
of the respective countries1. Data on Brazil differed between PSD ONLINE and ABIOVE, which AMARAL (2012) explained by different approaches for data collection. USDA PSD ONLINE data have
been used for international comparisons. The data from ABIOVE were used for the national market
1
Argentina - April to March; Brazil - February to January; USA - September to August.
36
Chapter 3
Methodology
analysis because these, according to AMARAL (2012) and OSAKI (2012), represent the reference for
national research institutes like CEPEA. For detailed information on national, state, macro regional and municipal levels data were sourced from IBGE, which provides detailed data of agricultural
production in its web systems SIDRA, ESTADOS and CIDADES as well as in annual reports on municipal agricultural production. Data on exports were obtained from the governmental web system
ALICEWEB2 provided by SECEX and MDIC. The NCM 4-digit code 1201 (category: 'Soya beans,
whether or not broken') was used. Further features (port: 'Santos', detail filter: county; periods)
were selected for detailed queries. The COMEX trade balances of MDIC were consulted for supplementary data verification.
3.3
Case studies
A case study is a quantitative or qualitative method that "investigates a contemporary phenomenon in depth and within its real-life context" (YIN, 2009, p. 18). This flexible research approach
compares or contrasts individuals, groups, actual situations, or cases. It is integrative to historical
and political issues or regional characteristics, by considering more variables of interest than only
data points, and relies on multiple sources of evidence. It is therefore useful for explorative studies (YIN, 2009, pp. 2). This method was assumed appropriate for accomplishing the objectives of
this study (see chapter 1) and a multiple-case design with four cases applied.
For the case studies one seaport and four production regions were chosen, as explained in chapter 2.2. Based on data from IBGE (2012), the county with the greatest national soybean production volume and a high contribution to the Brazilian soybean exports, Sorriso-MT, was identified
as major reference of the study. Using the database ALICEWEB2 (MDIC, 2012), the trading port Santos that covers most of the soybean exports of Brazil (28 %) and of Sorriso (83 %), the major Brazilian production region (see chapter 2.2), was determined as national destination of the export
process. In order to estimate the impact of transportation costs on total costs of soybean exports, three further locations within the corridor of Sorriso-Santos, which display different distances to Santos, were defined according to the criteria explained in chapter 2.2.
Comparing the selected regions shall enable an estimation of the impact of transportation costs
on total export costs of soybeans. As road transport represents the most important transport
modality (see chapter 2.3) for all of the selected regions, the focus of this study was set on this
transport mode. Rail transport via the Santos railroad plays an important role for the regions Sorriso and Rondonópolis and was therefore considered in the analysis, where data were available.
Waterway transportation, which only plays a marginal role for the case studies, was widely neglected and only mentioned in order to draw an integral picture of the whole process chain.
Chapter 3
3.4
Methodology
37
Semi-structured interview
As an approach to do qualitative research, the form of the semi-structured expert interview was
chosen to be the method applied. It ensures flexibility and openness during the interviews as the
questions can be adjusted to the interview context and situation as well as to the interviewed
person (UN FAO, 1990). This high flexibility allows probing for details within the interview, to discuss new issues arising as a result of preceding statements and to reshuffle topics to pursue new
ideas. The discussions were typically initiated by giving background information about the project
and a short presentation of the company by the interviewed partner. The further proceeding was
designed as an open interview. As recommended by LINDLOF (1995, p. 185), an interview guideline
was created prior to the interview, which served as a thematic framework for a fluid structure of
the interview by organizing the menu of topics to be covered. That way, a flexible handling of the
interview was possible while keeping the focus on the thematically important subjects. It further
enabled the comparison of the different interviews for analysis after the field research
(MEUSER/NAGEL, 2009, pp. 52, 56).
The main items discussed concerned structure of the process of soybean exports, major problems within these processes, essential cost positions, projections of the future development of
the Brazilian infrastructural system. The semi-structured interview enabled to gather new information and data during the field research as well as to verify, validate and adjust data and information obtained in prior literature research.
The face-to-face interviewed persons were chosen according to their function as within the process chain and their accredited market expertise (see table 3.1). As experts, researchers or agents
who are involved in the transportation and logistics processes were identified. Managers of
commercialization and managers of transport and logistics in the big trading companies and
transport companies composed the focus group. They should have an extensive general and exclusive knowledge of the soybean sector and be able to offer solutions and projections, relying on
a wealth of experience. Due to thematic misconduct or lacking expertise of the interviewee,
about 20 % of the interviews was not valid for this study and neglected.
Table 3.1:
Expert interviews by category
Discussion partner
Type
Objective
Main items of discussion
Research institute
Explorative
Introduction and overview of
the market and process chain
General information, structure and
characteristics of the Brazilian soybean
market and logistics
Systematizing
Profound information on
specific process chain stages
Specific information on operational
processes, freight prices, modalities of
transportation
Association
Producer
Trading company
Transportation company
Source: Created by author.
38
Chapter 3
Methodology
The aim of the interviews was to obtain comparable results by interviewing agents of the same
type in each region. The focus was set on the trading companies that organize the overall processes of origination, commercialization and transportation for obtaining information about market structure and behavior. Transport service providers were consulted for information about
specific transportation characteristics. Due to organizational constraints, like limited time, and
the broad range of agents that had to be interviewed and, it was not possible to interview experts from each category of table 3.1 in every region. For each selected county at least one expert related to a transport company and one expert related to a trading company was interviewed.
3.5
Cost reporting
HORNGREN (2009, p. 53) defined cost "as a resource sacrificed or forgone to achieve a specific objective[, which] (...) is usually measured as the monetary amount that must be paid to acquire
goods or services". A cost accounting gathers any information relating to the costs of acquiring or
using resources in an organization or market and ensures transparency through documentation
(HORNGREN, 2009, p. 30). By understanding the cost structure of a market, information on key success factors can be identified (HORNGREN, 2009, p. 73).
To identify each cost position within the logistic processes a cost report from the point of view of
the seller was elaborated (see table 3.2), based on the information from the market report of
SAFRAS&MERCADO (2012) and the expert discussion with ALVES (2012).
Table 3.2:
Calculation method
1. FOB Santos
2. Exchange rate
3. Turnover (FOB Santos)
US$/t
R$/US$
R$/t
4. Export expenses
4.1 Port fee
4.2 Comissions, brokerage
4.3 Taxes
4.4 Damage
5. Price at port gate
6. Freight rate
7. Total logistics costs
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
8. Theoretical price at local warehouse
9. Price quoted at local spot market
R$/t
R$/t
Difference [9]-[8]
Share [6]/[7]
R$/t, %
%
Source: Created by author. Based on data from Safras&Mercado (2012) and Alves (2012).
Chapter 3
Methodology
39
The free on board (fob) price (position 1) represents, according to INCOTERMS rules, the price,
which implies that the seller accounts for the costs of shipment to the port and loading onto the
ship. Until loading of the good, any risks of loss and damage to the goods are borne by the seller.
The applied exchange rate (position 2) corresponds to the monthly average data published by
IPEA (2012). Any further logistics costs and risks linked to the export process are charged from
the purchaser (ICC, n.d.).
Logistics costs include transport costs, warehousing costs, transshipment costs, fiscal cost and
port operations costs (KUSSANO/BATALHA, 2009, p. 31). The logistics costs (position 7) of the soybean export process were analyzed for each selected region based on the report. It was assumed
that the seller is user of transportation and storage services, the cost of which are represented by
the freight price (position 6) and the storage fee, respectively. Costs for storage were assumed to
be 0.00 R$/t because for soybean storage there is typically no fee charged in Brazil (see chapter
4.2.1). The position was therefore neglected in table 3.2. Transshipment costs were assumed to
be included in the costs for freight.
A theoretical price at the local warehouse of the selected region (position 8) was calculated by
subtracting the export expenses (position 4) from the turnover (position 3). The theoretical price
was used as a benchmark, with which the current price quoted on the local spot market (position 9) was compared. To evaluate the deviation of the calculated price from the real price, the
difference was calculated. For understanding the impact of freight costs on total logistics costs,
position 6 and 7 were set into relation.
Information about producer prices, commissions and brokerage and fob prices at Santos port
were sourced from CEPEA, while data on the port fee were primary data from the survey.
Freight (position 4.2) represents the costs for shipping a specific volume of freight from a specific
origin to the determined destination. Corresponding to data availability, monthly average freight
prices for road transportation on the routes Rio Verde-Santos and Barretos-Santos were sourced
from ESALQ-LOG, while data for the routes starting in Mato Grosso state were provided by IMEA.
Small differences in the data of these two institutions, where overlapping, were observed, which
might be attributed to different approaches of data collection. For reasons of comparison the
functional unit tonne-kilometer (tkm) was taken as the basis of the price calculations and applied
to the distances. Losses due to damage (position 4.5) were calculated with 0.5 % of the turnover
at Santos (position 3), based on the market report of SAFRAS&MERCADO (2012) and on the experts'
statements (ANONYMOUS 4, 2012; JESUS, 2012; GARCIA DA SILVA, 2012).
40
Chapter 3
Figure 3.1:
Methodology
Costs of truck transport
Fixed costs
Variable costs
Interest on capital
Fuel
Depreciation
Toll
Maintenance
Driver
Truck insurance
Truck licence
Source: Created by author. Based on GÖTZE (2004, p. 157) and expert interviews.
Subsequently, the costs of the road transportation were analyzed from the point of view of the
transport service provider to evaluate the major cost positions. The cost data were gathered in
the field research and aggregated into one exemplary transport cost report. As the interviewed
transport service providers employ their trucks all over the country, collected data have not been
distinguishable for each case study, except costs for fuels and tolls, which were accordingly classified as variable costs.
Following the approach of SCHMALENBACH cited in GÖTZE (2004, p. 157) the transport costs were
separated into their fixed- and variable-cost components (see Eq. I), as shown in figure 17. HORNGREN (2009, p. 57) distinguished variable costs from fixed costs by their relation to a given activity
or volume over a determined time span. Variable costs change in total proportion to changes in
the level of activity or volume while fixed costs remain unchanged for the respective time.
Fixed costs, which are not directly tied to the transport operation, include depreciation, interest
Eq. I
C = Cf + Cv
where
C = total costs (R$/tkm)
Cf = total fixed costs (R$/tkm)
Cv = total variable costs (R$/tkm)
on average fixed capital and - to a limited degree - labor costs. The average fixed capital is determined by the purchase price and the salvage value (GÖTZE, 2004, p. 58) (see Eq. II).
P−S n
Eq. II
Cap =
2
+ Sn
x
P+S n
=
2
x
where
Cap = average fixed capital (R$/tkm)
P = purchase price of vehicle (R$)
Sn = expected salvage value after n periods (R$)
n = asset depreciation range (years)
x = average annually distance travelled year [km]
Chapter 3
Methodology
41
The used price refers to the purchase price of a Bitrem truck and trailer composition with 37t
loading capacity, which is the typically used truck type for soybean shipping (see chapter 4.1.2.3)
(ANTUNES, 2012). The annual values were scaled to the functional unit of one tonne-kilometer.
Costs for maintenance and repair may be directly tied to the transport operation but were assigned to fixed costs because a fixed monthly rate per truck was reported and attribution to a
specific transport process was not possible. Any observable differences between regions were
elucidated. For the cost positions of labor, truck insurance and license, annual or monthly values
were utilized according to experts' statements.
Capital costs arise with the purchase of the vehicle and depend on the purchase price and the
annual interest rate. They include interest and depreciation of the acquired resources (see Eq. III
and Eq. IV).
Eq. III
I = Cap × i
where
I = interest costs (R$/tkm)
i = interest rate (%)
Depreciation of the vehicle is based on the purchase price of the new vehicle including truck and
trailers, on its asset depreciation range and its salvage value. Using the linear depreciation method, depreciation rates per period are constant.
Eq. IV
dt =
P − Sn
n
where
dt = depreciation rate in period t (R$/tkm)
Variable costs were calculated for each selected region by multiplying the specific costs of fuel
(R$/km) with the distance travelled and adding the arising toll costs (see Eq. V).
Eq. V
Cv = fuel costsa × distancex +
toll cost x
where
a = selected production region
x = route
Local average monthly fuel prices (R$/l) by the Brazilian NATIONAL AGENCY OF PETROLEUM, NATURAL
GAS AND BIOFUELS were provided by BEDOYA (2012). Toll costs were calculated with data from ARTESP (2012). Total transport costs were finally calculated according to equation I (Eq. I). Data was
not sufficient to provide a similar estimate for rail transportation.
3.6
Greenhouse gas inventory
For the calculation of the greenhouse gas emissions of domestic soybean transports from farm
gate to seaport, a GHG inventory was set up, which is based on the 1996 and 2006 guidelines of
the IPCC. The approach of the GHG inventory is part of the LCA methodology, which is an international standardized approach (ISO 14040, ISO 14044) for estimating the environmental impact of
a product over its complete life span. It is composed of goal and scope definition, life cycle inven-
42
Chapter 3
Methodology
tory analysis, life cycle impact assessment and life cycle interpretation (KLÖPFFER/GRAHL, 2009,
pp. 2, 12) and is used as a framework for this analysis.
Goal and scope definition
The GHG inventory addresses emissions due to domestic transportation processes for export
necessary to move the soybeans to the seaport. The study aimed to identify the GWP and potential savings of domestic transports embedded in the soybean export process chain (see figure 3.2). Identified optimization potentials can be used to contribute to the climate goals of the
Kyoto Protocol and to increase the attractiveness of the product for consumers by active marketing of the climatic improvement.
Figure 3.2:
Product life cycle
Extraction of raw material and sourcing of energy
Transportation
Production of good
Transportation
Consumption
Transportation
Recycling and removal
Source: Created by author. Based on KLÖPFFER/GRAHL (2009).
The analysis singled out the distances between Santos port and the selected production regions
Sorriso-MT, Rondonópolis-MT, Rio Verde-GO and Barretos-SP. As described in chapter 2.3 the
transport modalities of interest for these regions are road and railroad. As grease and lubricant
consumption is less than one per cent of the fuel consumption (BIAGGIONI/BOVOLENTA, 2010,
p. 592), these factors are neglected in the analysis. For reasons of comparison and to estimate
the climatic effect of soybeans the analysis is based on the functional unit of one ton of soybeans
to which all estimates are related.
The field data provided first-hand information for the GHG inventory and were utilized for the
calculation of the GHGs emissions. The values used were obtained in the field research and, if
lacking, complemented by data published by CNT and ANTT. To date, only few studies (SILVA ET AL.,
2010; BIAGGIONI/BOVOLENTA, 2010) exist, which address the environmental impact of soybean
transportation and logistics processes and use the LCA approach.
Chapter 3
Methodology
43
Life cycle inventory analysis
With regard to the available limited data quantity the Tier 1 method of IPCC (2006) was chosen as
an appropriate approach for the GHG inventory and applied to the selected process. The calculation is based on quantity and the type of fuel combusted as well as average emission factors. Tier 1 focuses on the GHGs CO2, CH4, N2O (see table 2.9). The indirect GHG NOx and the acidification factor SO2 are included, but not described in detail. Applying the Tier 1 approach, emissions
are calculated in terms of CO2 equivalents (see Eq. VI). The estimated fuel consumption was multiplied with a default factor, which is equal to the total carbon content (see footnote 19) of the
fuel multiplied with the ratio of molecular weight of CO2 (44) to the molecular weight of Carbon,
which is 12 g/mol (IPCC, 1996, p. 3.12). The CO2 equivalent used for calculation includes CH4, N2O
and CO2, the latter of which constitutes more than 95 % of the emissions from fuel combustion
(SILVA et al., 2010, p. 1836).
E=
Eq. VI
Fuel × EF
where
E = GHG emissions (kgCO2eq)
Fuel = fuel consumed (l)
EF = emission factor (kgCO2eq/l)
According to BARTHOLOMEU (2006, p. 54) a 100 % oxidation, which is required for this method
(IPCC, 2006, p. 3.35), can be assumed because only a small fraction (≤ 1 %) of the fuel entering
the combustion chamber escapes from oxidation. The emission factor determination is based on
a number of assumptions concerning input data like vehicle technology mix, driving conditions,
including travelling speeds, and climatic conditions. The default value for diesel of 2.67 CO2eq/l
suggested by IPCC (1996) is a value for European heavy-duty diesel trucks. Because fuel qualities
and transport conditions differ between countries, local emission factors and energy data have to
be considered to ensure comparability. Appendix 29 presents the values, which were taken as
reference. In this study the emission factor 2.80 CO2eq/l, suggested by BARTHOLOMEU (2006, pp.
51), was applied. BARTHOLOMEU (2006, pp. 51) argued that low quality diesel is used in Brazil and
adjusted emission factors to a higher value compared to IPCC default value.
Corresponding to the major export routes of the four case study regions, the analyzed routes are
Sorriso-Santos (2,200 km), Rondonópolis-Santos (1,400 km), Rio Verde-Santos (1,050 km) and
Barretos-Santos (500 km). For calculating the emissions of one tkm of soybeans equation Eq. VII
was used (IPCC, 1996). The results of the carbon emission inventory are presented as absolute
values of total carbon dioxide-equivalents measured in kilograms per ton (kgCO2eq/t).
Eq. VII
E=
EF × Fuel × Distance
Volume transported
where
E = GHG emissions of CO2eq per route (kgCO2eq/t)
Distance = kilometers travelled (km)
EF = emission factor (kgCO2eq/l)
Fuel = fuel consumed (l/km)
Volume transported = volume of cargo (tkm)
44
Chapter 3
Methodology
A heavy-duty truck, model Bitrem with 37 t net weight, which operates with diesel and consumes
0.5 l/km, i.e., 0.0135 l/tkm, was assumed for road transportation (ANTUNES, 2012). As road
transport is typically operated by haulers who employ their trucks using almost 100 % of the capacities, a one-way travel between origin and destination with 100 % loading factor was assumed. GHG emissions emitted during return transports, where trucks are loaded with different
products, were accordingly not assigned to the exported soybeans.
For rail transportation, a train composition with a diesel engine locomotive and 70 wagons with a
total net weight of 7,000 t was considered for soybean shipping on the Alto Araguaia-Santos railroad as suggested by GARCIA (2012). Due to varying gauges and restricted access to railroads that
are operated by other concessionaires (see chapter 2.3.2) the rail equipment is not employed on
other railroads than the ALLMN and ALLMP. Because only about 30 % of the capacities of the
returning train composition is utilized for the transportation of other products (GARCIA, 2012),
70 % of the emissions emitted during the return travel have to be attributed to the soybean
transport.
To calculate the emissions from rail transports a fully-loaded train transporting soybeans from
Alto Araguaia to Santos with a diesel consumption of 0.007 l/tkm was considered in a first step
(ANTT, 2010, p. 21; GARCIA, 2012). In a second step assumptions for the return transport from
Santos to Alto Araguaia were changed. Due to a small loading factor (30 %) with other goods like
fertilizers, lower fuel combustion of 0.005 l/tkm was considered and 70 % of the gases emitted
during the travel were attributed to the soybean export process.
Chapter 4
4
45
This chapter shall elucidate the pattern of the logistics market by reporting the market structure,
based on the data achieved during the field research. In order to estimate the importance of logistics and transportation processes for the soybean exports and its impact on competitiveness in
terms of costs and CO2 emissions, the logistic costs in general and the costs of road transportation specifically as well as the emission exhaust are analyzed using the examples of four selected
regions (see figure 4.1) and their export routes to Santos port (see appendix 30).
Figure 4.1:
Exemplary route integrating selected production regions
Source: Google Maps (2013).
Figure 1. Exemplary route integrating selected production regions.
46
4.1
Chapter 4
Market analysis
Soy is a seasonal crop, which is in Brazil produced between September and May. The soybean
marketing year, however, begins in February and lasts until January of the following year (AMARAL, 2012). Transportation services for soybean shipping are principally demanded during harvest (January to May), as a large part of the harvest is immediately shipped to the export ports or
warehouses of the industries (see chapter 2.3.4). Few large international companies dominate
the soybean trading whereas the market structure of the transport service sector is fragmented.
The road transport service sector is highly competitive. Road freights are therefore determined
by the company's costs and the market behavior. By contrast, the railroad of the Sorriso-Santos
corridor is operated by a monopolistic operator. Rail freight prices are individually negotiated and
often more expensive than shipping by truck.
4.1.1
Agents
The Brazilian soybean business is dominated by a limited number of trading companies, which
account for approximately 80 % of the soybean commercialization. Cooperatives are covering
20 % of the traded volume (AMARAL, 2012).
The top four players on the Brazilian market are the international companies ADM, BUNGE, CARGILL
and LOUIS DREYFUSS COMMODITIES. Besides other agricultural products and inputs, these large companies handle large volumes of soybeans (≥ 2 mmt/year) and account for a market share of 61 %
(see figure 4.2). Four medium-sized companies that rank within an annual trading volume between one and two million tons of soybeans per year hold a market stake of 21 %, while about
thirteen small companies (≤ 1 mmt/year) cover the residual 18 % (AMARAL, 2012). Many of the
large and medium-sized trading companies, like the Brazilian ANDRÉ MAGGI GROUP, integrate back
in the value chain. The group incorporates a medium-sized trading companies and one of the
world's largest soybean producing companies. Besides, it maintains an own freight company (FERREIRA, 2012). ADM, CARGILL and BUNGE own large warehouses and crushing factories. They further
operate port terminals in Brazil. As a result these companies possess a strong market position,
which according to PEETERS (2012) enables them to force prices down or demand more services.
Chapter 4
Figure 4.2:
47
Market share by company size
Medium-sized traders
21 %
Large traders
61 %
18 %
Small traders
Source: Created by author. Based on AMARAL (2012).
Producers negotiate independently with their clients or take advantage of cooperatives or farmer
unions. In the Cerrado region, including Rio Verde, Rondonópolis, and Sorriso, the typical farm
size is 1,000 ha with 65 % of the farms being even larger (DOHLMANN/SCHNEPF/BOLLING, 2001, p. 20;
USITC, 2012, p. 2-4). A number of farms have more than 10,000 ha (DRESCHER, 2012). These large
producers are often organized in small farmer unions, forming powerful lobbies and negotiation
parties. Particularly in the case of Sorriso, where large farm sizes are common, trading companies
often directly negotiate with the local producers or farmer unions. About 15 trading companies
are active in Sorriso while in Rondonópolis it is only about half of it (ALVES, 2012).
Only few cooperatives are settled in Mato Grosso (AMARAL, 2012), whereas in Rio Verde a major
competitor on the soybean market is the large cooperative COMIGO. COMIGO is active as trader,
processor and supplier of agricultural inputs. Due to its large crushing capacities and further infrastructure for processing of soybeans in the region, the cooperative principally focuses on the
domestic market and directs the soybeans to the factories. The soybean volumes that are destined to export, however, are mainly traded by private trading companies (SILVA, 2012). The number of cooperatives increases when going southeast (ALVES, 2012). In the South and Southeast,
including Barretos-SP, farms are generally small with an average size of 40 ha due to higher population densities (see chapter 2.2) and more expensive land prices. The small-scale producers are
closely related to the local cooperatives that buy and market the agricultural products (DRESCHER,
2012; USITC, 2012, pp. 2-4, 6-9).
On the market of road transportation services acts a plethora of small companies while the market for rail services on the Sorriso-Santos corridor tends to have a monopolistic structure. Rail
transportation services are contracted annually with fixed freight rates (ANONYMOUS 5, 2012). This
occurs in the case of road transportation rarely, e.g. in the case of the small trading company for
which ANONYMOUS 6 (2012) worked. In the majority of cases hauling services are contracted just in
time with a weekly frequency (ANONYMOUS 4, 2012). Confirming CAIXETA-FILHO (2008, p. 8), PARREIRA
(2012) stressed that the market is highly competitive where companies are under pressure to
keep costs at a minimum. Big companies with large handling volumes often cooperate with
48
Chapter 4
transportation agencies, which manage the transport process and aggregate the needed capacities of trucks and drivers. While NUNES (2010, p. 58) and IPEA (2011, p. 127) reported equal market shares of the market participants, ANONYMOUS 4 (2012) estimated that on national level about
70 % of the road transports are operated by self-employed drivers and 30 % by transportation
companies with own fleet. In Mato Grosso the proportion is reverse. ANONYMOUS 4 (2012) instanced that 50 % of the transportation processes of the trading company for which he worked
(volume of soybeans traded ≥ 2 mmt/year) are realized with self-employed drivers and another
50 % with trucking companies.
4.1.2
Logistics processes
The logistic process of procurement, storage and distribution of soybeans includes various stages,
in which either one or several agents are involved. The shipping process starts at the farm gate
from where the product is transported via road to its next destination. Most of the harvested
volume is carried to a regional warehouse, where post harvest management measures are applied and classification takes place. Only small volumes (no detailed data available) are directly
shipped to the seaport. Figure 4.3 illustrates an exemplary structure of the export process including the involved participants, assuming that the soybeans are stored off-farm and sold to a trading company that organizes the handling of the product from farm gate until ship loading.
Figure 4.3:
Farm
Producer
Exemplary export process from farm to seaport.
Warehouse
Transport
Transport company
Transport
Warehouse operator
Transport company
Export
port
Loading onto ship
Port operator
Trading company
4.1.2.1
Grain origination
The soybean producers of the Center West sell their grains mainly to trading companies. In the
southern soybean production regions producers frequently sell to cooperatives that also act as
traders. In some cases, intermediaries like local elevators or commodity brokers participate in the
grain origination process. In other cases soybeans are directly sold from the production site to
soybean processors or exporters (MAPA, 2007, p. 66). Together, the national process chain involves private service providers such as trading companies, brokers and warehouse operators, as
well as freight forwarders, transporters, and bankers on the one hand, but also public agencies
like customs, port authorities and transport regulators on the other hand.
Chapter 4
49
Generally, producers market their soybeans at harvest on the local spot market or earlier through
forward contracts (ALVES, 2012). These contracts imply an agreement between producer and purchaser, which defines price and quality considerations of the commodity to be fulfilled at a fixed
delivery date. NIEDERMEIER (2012) confirmed the statement of MANGAN/LALWANI/ BUTCHER (2008,
p. 92) that a forward contract works as a buffer against uncertainties such as volatile demand
levels or price fluctuations. The originator purchases the soybeans either fob warehouse farm or
cif (i.e., cost insurance freight) warehouse purchaser (FRANÇA, 2012; ANONYMOUS 3, 2012). Both
manners are differentiated by the liability for transportation to the warehouse, classification,
drying and storage. In the case of fob warehouse farm it is the purchaser who is liable, while in
the other case (cif) it is the producer's liability. The other option is to sell the soybeans on the
spot market. A farmer's decision to sell the soybeans immediately at harvesting or at a later point
of time is determined by the availability of sufficient capacities of storage and of capital
(ANONYMOUS 1, 2012; FRANÇA, 2012). Per unit costs of financing and insuring inventories are high
over time (USITC, 2012, p. 3-14), which makes the expectation of the increase of prices by more
than the cost of storing the decisive factor. If the future price is lower than the current cash price,
the producer has the incentive to sell immediately to minimize his loss. At least, the decision to
store is based on the expected returns from storage and financial power of the seller.
4.1.2.2
Warehousing
Grains are stored in warehouses, which are located in close proximity to the production areas
and belong to private companies, cooperatives, producers or governmental institutions. Large
trading companies provide additional transshipment points, which are located within an average
radius of 50 km to the farms. These serve for the collection of the agricultural products from the
farms nearby in order to take advantage from aggregated shipment to the trader's warehouse.
The system is particularly beneficial to farms distant to warehousing infrastructure (GARCIA DA
SILVA, 2012; ANONYMOUS 5, 2012).
Basically, warehouses in Brazil are operated by private companies (76 %) and cooperatives
(20 %), who offer their services to the producers (CONAB, 2011). Warehouses under governmental
administration account only for a small part and are not relevant for this study. The majority of
the privatized warehouses are owned by big trading companies, which handle large volumes and
are therefore able to utilize the warehouses to capacity in order to keep the fixed costs at a minimum. Access to the private warehouses is often restricted to the company's clients. In contrast,
cooperatives work as service providers in general, offering public access to storage for money
(FREITAS, 2012). The share of on-farm storage capacities levels approximately fourteen percent of
the total storage capacities (see appendix 24). Large funds are required for investing in storage
capacities and operating an on-farm warehouse, so that only a slight percentage (no detailed
data available) of farmers operates an own warehouse in the South and Southeast, where most
farms are small (see chapter 4.1.1) (DRESCHER, 2012). This percentage is much higher in Mato
Grosso. ANONYMOUS 1 (2012) guessed that one third of the storage capacities in Mato Grosso is
50
Chapter 4
located on farms (see appendix 31). KLASENER (2012) estimated that in the region of Sorriso 60 %
of the farms have warehouses, typically with storage capacities for 50 % of the farm's production
volume. In contrast, in Goiás the share of farmers who are operating an own warehouse is small
but the medium size of on-farm capacities is large (90,000 t). SILVA M. (2012) stated that only ten
out of 1,060 farmers in the region of Rio Verde store on-farm, assuming that a share of less than
two percent of the producers in the whole state Goiás own a warehouse. There were no data
available for Barretos and Rondonópolis. Table 4.1 represents the storage capacities in the selected regions.
Table 4.1:
Storage capacities in selected regions vs. production output of soybeans and
corn (2011)
Warehouse
type
Conventional
Specialized on
bulk goods
N° of
warehouses
Capacity
t
N° of
warehouses
23
31
20
4
135,911
219,679
87,222
6,322
188
54
65
6
Sorriso
Rondonópolis
Rio Verde
Barretos
Capacity
t
3,382,626
975,639
1,423,699
139,715
Total
N° of
warehouses
211
85
85
10
Capacity
t
3,518,537
1,195,318
1,510,921
146,037
Soybean and
corn output
2011
t
2,978,346
293,760
1,494,050
22,500
(Status 12/20/12)
Source: Conab (2012b).
4.1.2.3
Transportation
With farms normally having no direct access to rail terminals or inland ports, short distance
transports from farm to warehouse have to be performed by truck. Only a small part goes directly to a factory for processing or to the export port. Long-distance transports from warehouse to
port are either realized by trucks or by intermodal transportation, which includes road and rail or
road and waterway transport. The cargo is shipped to the export port where it is first stored or
directly loaded onto the cargo ship. Operations within the port area are conducted either by the
port operator or by the concessionaire, which is in most cases a trading company (ANONYMOUS 2,
2012).
Road transportation
For shipping agricultural bulk goods, transport service providers commonly use a double trailer
truck with either seven or nine axles. ANTUNES (2012) asserted the use of the seven axle model
Bitrem (see figure 4.4). It has a net weight cargo of 37 t and a maximum gross weight of 57 tons.
Medium fuel efficiency was estimated at 0.5 l/km. The truck model with nine axles Rodotrem has
a maximum gross weight of 74 tons with net weight cargo of 50 t. Even though the capacity is
greater, it is less used because reception infrastructure in intermodal terminals is lacking and
Chapter 4
51
working hours during the day are legally restricted. Furthermore, complicated licensing procedures for new registrations hinder the fleet owners to take advantage of the Rodotrem model
(JESUS, 2012; ANTUNES, 2012; ANONYMOUS 4, 2012).
Figure 4.4:
Truck model Bitrem
Source: ANTUNES (2012).
Source: ANTUNES (2012).
The principal route, which can be travelled by truckers from any of the regions and which was
cited by most of the experts, is the route Sorriso-MT - Cuiabá-MT - Jataí-GO - São José do Rio Preto-SP - Araraquara-SP - Santos-SP (illustrated in figure 4.1). The route includes the highways BR163, BR-364, BR-153, SP-310, SP-330, SP-348 and SP-160. These are classified by CNT (2012) to be
in regular, good or very good conditions. As reported in chapter 2.3, the highways of São Paulo,
which are operated by concessionaires, have the best conditions (CNT, 2012a, p. 8) but are tolled
(see appendix 32). In contrast, the state governed highways and roads to the farms in Goiás and
Mato Grosso are in clearly worse conditions. Examples are documented in appendix 33. But GARCIA DA SILVA (2012) stated that large tracks are already being improved.
The export routes from Sorriso to Santos coincide with the Rondonópolis-Santos routes from
Rondonópolis on. An attractive alternative route for truckers to the one cited above is going from
Rondonópolis through Mato Grosso do Sul (Campo Grande-MS) to São Paulo (see appendix 30).
This route offers improved road conditions compared to the former (ANONYMOUS 4, 2012) but
more kilometers have to be travelled. Soybeans shipments from Rio Verde to Santos follow mainly the principal route cited or are alternatively guided via Uberlândia-MG and Ribeirão Preto-SP
to Santos. Transports from Barretos join the principal route in Araraquara-SP or drive past Ribeirão Preto to Santos (GARCIA DA SILVA, 2012). Table 4.2 represents for each region the average
transit time, i.e., the time spent on the road, and averaged speed driven of a travel to Santos. For
loading of the truck most experts suggested a time of one day including waiting times at the
warehouse. Unloading at Santos was calculated with one day. ROGÉRIO (2012) and others added
that waiting time at the port often takes up to 48 hours during peak harvest season or even longer.
52
Chapter 4
Table 4.2:
Transit time from selected regions and average speed driven.
Origin
Distance to Santos
Transit time
Average speed
km
days
km/h
2,200
1,400
1,050
500
6
5
3
0.75
40
40
45
n/d
Sorriso-MT
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Source: Created by author. Based on expert interviews.
Intermodal transportation
The combination of two or more transport modes is an alternative option for soybeans originated
in the Center West. Referring to the conclusions drawn in chapter 2.3 that rail transportation is
more efficient for long distance transports than road transportation, intermodality is a considerable option for shipping the soybeans from the remote Center West regions to Santos port. The
ALLMN railroad and the TIETÊ-PARANÁ waterway offer the option of intermodal transportation of
agricultural products to Santos via road and rail or road and waterway, respectively. Trucks carry
the soybeans to the terminals in Alto Araguaia-MT (rail) or São Simão-GO (waterway), where it is
transshipped and carried towards Santos (see figure 4.5).
Figure 4.5:
Intermodal export corridors
Source: Aprosoja (2012).
Figure 1. Intermodal export corridors.
Chapter 4
53
The selected regions, which are able to take advantage of the railroad, are Sorriso and Rondonópolis, while soybean shipping from Rio Verde can be accomplished by integrating water
transportation. The ALLMN railroad is principally utilized by the big trading companies, which
ship up to 80 % of their handled volumes by train (ANONYMOUS 1, 2012; ANONYMOUS 3, 2012). BIRKHAN (2012) explained that great shipping volumes are required to make rail transportation a
profitable option for traders. Contracts with determined volumes to be transported are fixed for
one year. After that period contracts are renegotiated. Freight prices and volumes are then adjusted (ANONYMOUS 3, 2012; ANONYMOUS 6, 2012).
Basically, the company ALL owns the rail equipment and provides transportation services to its
clients. But according to ANONYMOUS 1 (2012) and GARCIA (2012) big traders often provide the rolling stock themselves or rent private rail terminals. ANONYMOUS 3 (2012) confirmed that the trading company for which he worked (volume of soybeans traded ≥ 2 mmt/year) owns wagons,
which are operated by the transport service provider. According to WORLD BANK (2010, p. 39), this
lowers the risks to the concessionaires and thereby allows lower tariffs. That implies that these
companies have privileges to use the railroad while smaller traders with minor trade volumes and
insufficient funds end up not using the railroad.
For soybean transports ALLMN utilizes wagons with a loading capacity of 60 to 120 t. The type of
rail wagon used for bulk is shown in figure 4.6. GARCIA (2012) asserted that a typical train composition transports during peak of the soybean export season a volume of approximately 7,000 t. It
is, according to ANONYMOUS 2 (2012), transported in about 70 wagons with intake capacities ranging between 60 t to 120 t, which are pulled by diesel-electric driven engine machines. The travel
ends in the port area of Santos, where the cargo can be directed to the different terminals
(ANONYMOUS 2, 2012).
Figure 4.6:
Wagon for bulk transports.
Source: ANTF (2012).
Figure 1. Wagon for bulk transports.
Until arriving at the border of São Paulo trains travel at maximum with a driving speed of
80 km/h. Due to urban areas and a less resilient antique railroad infrastructure, the speed has to
be slowed down to maximum 40 km/h. Often, maximum speed cannot be driven because of limiting factors like civil works on the railroad, bad signposting or people on the track. SILVA (2010, p.
54
Chapter 4
10) reported a slowdown to 14 km/h, when getting closer to Santos. Medium fuel efficiency was
estimated at 0.007 l/tkm (ANTT, 2010, p. 21; GARCIA, 2012). While GARCIA (2012) outlined the fast
time of travel, ANONYMOUS 4 (2012) guessed that moving soybeans by train from Alto Araguaia to
Santos takes about seven days because of infrastructural deficiencies while a truck starting in
Rondonópolis can perform a transit time of five days (see table 4.2).
The TIETÊ-PARANÁ waterway export corridor to Santos starts in São Simão-GO and ends in Pederneiras-SP, from where soybeans are shipped by truck or train to the seaport. Besides for soybeans originated in Rio Verde and adjacent regions, according to APROSOJA (2011) the waterway is
also frequently used for soybean exports from the eastern region of Mato Grosso including, e.g.,
Querência. Even though this is the cheapest way of shipping, efficiency is according to ANONYMOUS 1 (2012) little because further transshipments to rail or truck at Pederneiras are necessary.
Thus, only a small portion of the soybean harvest in the respective regions is shipped that way.
The option is furthermore limited in use to the big trading companies due to high fixed costs
(GARCIA DA SILVA, 2012). A large trading company (volume of soybeans traded ≥ 2 mmt/year), who
operates a private terminal in São Simon, sends only five percent of its Mato Grosso soybean
exports that way (ANONYMOUS 4, 2012). ANONYMOUS 5 (2012) reported that, due to high rail freight
prices (see chapter 4.2.1), large trading companies, tend to shift from transporting the major volumes via rail to water and road transportation.
4.1.2.4
Port
At the port the bulk good is classified and sent to the warehouse, where it is stored for a short
period of time until loading onto the ship, or directly loaded onto the ship. ANONYMOUS 1 (2012)
guessed that in the gutters of the terminal of the trading company for which he worked (volume
of soybeans traded ≥ 2 mmt/year), where data were exemplarily collected, every hour about
30 trucks discharge their loading volume, which equals a capacity of circa 1,000 t/h, in peak harvest time. Three rail reception channels enable a maximum intake of 1,650 t/h from trains (channel one: 700 t/h or 160 wagons/day; channel two: 600 t/h or 140 wagons/day; channel three:
350 t/h or 80 wagons/day). About 250 wagons/day are on average unloaded during harvest time.
The turnaround time of a wagon at the company's port terminal is on average eight hours. Storage capacities are utilized only for aggregating the cargo volume until ship loading. Medium storage time is six days. The three warehouses at the terminal offer storage capacities of 50,000 t,
72,000 t and 50,000 t. The large bulk carriers, like PANAMAX, have loading capacities of 60,000 t to
80,000 t. Smaller handy size vessels have loading capacities of more than 10,000 t. Capacities of
48 t/day or 2,000 t/h for shipment enable to load a PANAMAX vessel in about 1.5 days (ANONYMOUS 2, 2012).
Chapter 4
4.1.3
55
Soybean season
The Brazilian soybean cropping cycle starts in late September and lasts until May (see table 4.3).
In the Center West, a typical crop rotation system includes the production of soybeans as summer crop (September - February) and corn as winter crop (February - August) (SILVA, 2012). The
exact dates of planting and harvesting soybeans vary from region to region from September to
November, depending on the day of the first rainfall after the drought period, which initiates the
planting process. Generally, first planting occurs in Mato Grosso in late September with harvest
season starting in January (GARCIA DA SILVA, 2012; OSAKI 2012; CONAB, 2012a, p. 26). Due to rain falls
at a later date, planting in the southern states typically starts in late October. In these regions
harvesting lasts from February (Paraná) until early May (Rio Grande do Sul) (DRESCHER, 2012;
CONAB, 2012a, p. 26). Some regions in Goiás and Mato Grosso, like Montividíu-GO, are characterized by such a favorable climate that production of up to three different crops per year is possible, as far as irrigation is provided. Additionally, the second crop needs to be harvested until early
July. OSAKI (2012) suggested sorghum and millet as third crops.
From an agronomical point of view it is also possible to plant precocious soybean but it is widely
avoided and even restricted by law to grow soybeans as winter crop to prevent rust and fungi.
PEETERS (2012) reported oats and beans with harvest in September to be planted in Rio Verde.
Table 4.3:
Soybean crop calendar
Brazil
Planting
Central Brazil
Southern Brazil
Brazil
Harvesting
Central Brazil
Southern Brazil
Months
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Source: Created by author. Based on Conab (2012a, p26) and Nunes (2012).
The demand for transportation and logistics services corresponds to the seasonality of the soybean crop cycle. The total export season extends over the period of February until September
(NUNES, 2012). According to ANONYMOUS 3 (2012) the peak of global demand for Brazilian soybeans is in late February and March when soybeans have gotten scarce in the importing countries. The more intensive period for soybean exports and transports is from March to June (see
figure 4.7 and appendix 34) when the harvested soybeans have already passed through post harvest management and classification and are sent to the seaports. From September on, when major part of the Brazilian exports is already sold and U.S. soybean harvest begins (USDA FAS, 2012),
export volumes shrink.
56
Chapter 4
Figure 4.7:
Brazil - monthly soybean exports (2009-2011)
tons
7 000 000
2011
6 000 000
2010
5 000 000
2009
4 000 000
3 000 000
2 000 000
1 000 000
0
Jan
Fev
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
4.2
Cost analysis for four production regions
Starting the analysis at the port with the fob price, each cost position was documented to finally
calculate a theoretical price received by the producer in the local warehouse of the respective
production region.
4.2.1
Cost positions and cost structure
The logistics costs of the soybean export are presented in figure 4.8 with the major cost centers
being port charges, transportation and storage costs. Generally, fiscal costs have to be
considered but as explained in chapter 2.1 soybeans that are exported are exempted by the Kandir Law from taxes, so that fiscal costs equal to 0.00 R$/t in the analysis. Transshipment costs
may arise at various stages of the transport process but are generally included in the port fee and
in the freight rate, which represents the transport costs.
Figure 4.8:
Logistics cost structure
Logistics costs
Port charges
Fiscal costs
Transshipment charges
Freight rate
Costs of truck
demurrage
Loss of cargo
Storage costs
Chapter 4
57
Port charges
In Santos, soybeans are handled at private terminals, which are operated by concessionaires.
Prices for port operations are formed through negotiation and depend on various factors, including volume and cadence of the cargo to be handled (ANONYMOUS 1, 2012). KUSSANO/BATALHA
(2012, p. 622) stated that the port fee comprises all costs relating to the use of the port
infrastructure, including the costs for transshipments and warehousing. The estimated values of
the port costs gathered in the field research varied from 15 R$/t to 25 R$/t1. The difference in
values may be due to different volumes handled or a privileged access to the port terminal for an
agent, e.g., a trading company who runs an own terminal. The cost position, which impacts most
on the total costs for running a terminal, is the lease cost (approximately 70 %). Major operational costs are energy and personnel costs (ANONYMOUS 1, 2012; ANONYMOUS 2, 2012). Based on the
information given by the experts (FRANÇA, 2012; ANONYMOUS 1, 2012) and congruently with CEPEA (OSAKI, 2012), the port fee for the calculation was determined at 11 US$/t (22 R$/t).
Experts complained the strong position of the port syndicate. The port operator is obliged to contract a large part of the required stevedores from the syndicate's pool (NUNES, 2012). AMARAL
(2012) asserted that these regulations inhibit the tertiarization of services and mechanization of
processes. It further causes elevated operational costs as, e.g., the tariff regulations for syndicate
personnel represent high costs for the terminal operator. Lacking commitment to work, frequent
strikes of the syndicate personnel and high rates of absence result in inefficiencies and trouble
port processes. Especially strikes may lead to high extra costs. ANONYMOUS 1 (2012) exemplified
that as from a waiting time of 18 hours a demurrage cost of 50 R$/h is charged per unloaded rail
wagon by the rail operator with 150 to 250 wagons being unloaded during harvest time.
Restricted inland port access (see chapter 2.2.5) and limited reception capacities at the port lead
during peak harvest season to long line-ups of trucks both at the port gates and at intermodal
terminals. A demurrage fee for overtime costs of a waiting truck has to be paid by the trader or
exporter after 24 hours and levels 0.40 R$/t/h, or 14.80 R$/truck/h (PARREIRA, 2012; ANONYMOUS 6,
2012; GARCIA DA SILVA, 2012; ROGÉRIO, 2012). According to OSAKI (2012), the elevated costs are
passed on to the producer who will receive a lower price for his products.
Experts estimated that the average time of a truck waiting to unload the soybeans at the terminal
is approximately two days in peak harvest season if there are no further distortions, e.g., rain, of
the process (ANONYMOUS 6, 2012; GARCIA DA SILVA, 2012). ANONYMOUS 1 (2012) and ANONYMOUS 2
(2012) reported truck line-ups of 10 to 30 days. Rainy weather induces a loading stop because the
terminals are not protected against rain (GARCIA DA SILVA, 2012; ANONYMOUS 2, 2012). ANONYMOUS 2
(2012) underlined the effect of a stoppage of ship loading by elucidating that during harvest season an estimated volume of 30,000 t/day of soybeans is moving by train or truck to the port. A
1
In some cases, experts reported values in US$. These values were accordingly recorded. For reasons of comparison
approximated values in R$ are recorded in brackets [applied exchange rate: 2.03 R$/US$ (August 2012; IPEA, 2012);
applied exchange rate in calculations: according to month and year of reference of the calculation].
58
Chapter 4
delay at the port of ship loading would bring 60,000 t of cargo volume and of trucking capacities
to a standstill at the port gates and lead to high opportunity costs.
Freight
The transport service market is a demand-driven market. Freight rates are volatile and subject to
seasonality. According to CAIXETA-FILHO (2003, p. 1) "freight prices are formed through free negotiation determined by supply and demand for the transport service". The rates are composed by
the transport costs and a profit margin of the service provider. The seasonal supply/demand relation may increase or decrease the profit of the transportation company (JESUS, 2012).
Road transport
The formation of the road freight price which the buyer, i.e., the trader or exporter is willing to
pay was described by ANONYMOUS 4 (2012). On the basis of historical data the buyer estimates the
freight costs for the planning period, which usually corresponds to the calendar year. The historical freight rates at peak harvest time are evaluated and the market analyzed. It is common to
exchange ideas with other market participants in order to optimize the estimates. Based on this
information the freight rate, which the buyer is willing to pay, is calculated. As the transport market is a volatile market (see figure 4.9), buyers and suppliers both have to steadily adjust their
positions to the market. They have to stay current on changes in every shipping cost variable and
in market behavior to negotiate efficiently with the counterpart. ANONYMOUS 4 (2012) asserted
that the few big trading companies (see chapter 4.1.1) have as large buyers the negotiation power to exert pressure on transport haulers to obtain freight transport discounts. Thus, freight rates
are influenced by the customers' willingness to pay as well as by the demand and supply relation.
Figure 4.9:
Freight rates for selected regions (2011-2012)
R$/t
300
Sorriso
Rondonópolis
246
250
230
213
200
198
194
200
186 188
194
176
197
185
181
150
146
133
140 142
137
114 110
109
108 110 110
100
94
96
100
129
147
128
62
61
61
61
61
61
61
61
188
148
142 142
132
113 112
108 112
108
109 108
97
133
98
102 105
61
192
175
155
154 149
Barretos
207
172
165
163
147
202 198 202
61
61
72
61
62
50
01/2011 03/2011 05/2011 07/2011 09/2011 11/2011 01/2012 03/2012 05/2012 07/2012 09/2012
Source: Created by author. Based on data from ESALQ-LOG and IMEA.
Rio Verde
Chapter 4
59
On this highly competitive market transportation companies are price takers who have to at least
cover their costs. The composition of the transport costs is illustrated in table 4.4. JESUS (2012)
stated that in his company 70 % of the costs of the truck are determined by the gross driver labor
rates, costs of fuel, maintenance and tires. PARREIRA (2012) confirmed that the major cost positions are fuel and personnel costs. But experts emphasized that the biggest freight price drivers,
which e.g. in 2011 and 2012 led to high freight rates, are market demand and opportunity costs.
The opportunity costs refer to the loss of benefits, which would have been received by choosing
the next best alternative. Lacking capacities for transportation frequently result during harvest
peak in excess demand, leading to inefficiencies and driving prices. The experts exemplified that
the price surge during 2012 was no result of changes in transport costs, but was caused by shortages in capacities of trucks and labor due to very large harvest volumes of soybeans and corn
safrinha, and due to a new truck drivers law (see chapter 5.6) (ROGÉRIO, 2012; ANTUNES, 2012).
Moreover, waiting times at the port gates of 48 hours up to five days are common during harvest
season and (see chapter 4.1.2.3) (PARREIRA, 2012). Any waiting time less than 24 hours is paid by
the transport service provider who can include these costs in the charged freight rate (ROGÉRIO,
2012).
Table 4.4:
Composition of road freight rate
1. Costs of truck
2. Operational costs of company
3. Fiscal payments
1.1. Capital costs
1.2 Maintenance costs including tires
1.3 Fuel costs
1.4 Labor costs
1.5 Costs of licence
1.6 Opportunity costs
2.1 Energy
2.2 Personnel
2.3 Administrative costs
2.4 Experience
2.5 Financing of delayed payments of clients
2.5 Other costs
3.1 Taxes on company's profit
3.2 ICMS, PIS/CONFINS
3.3 Social contribution payments
Source: Created by author. Based on expert interviews.
Rail transport
The competitiveness of rail transportation is restricted by elevated rail freight prices in Brazil.
According to ANONYMOUS 5 (2012) the rail freight rate increased significantly within the last years.
The ALLMN railroad is owned by the investment company GP INVESTIMENTOS and operated by ALL.
Because the company holds a monopoly, prices are adjusted accordingly (ANONYMOUS 5, 2012).
The monopolistic market position enables to settle the freight price above marginal cost without
losing all customers.
Because the rail market is rather closed, no exact data were revealed in the interviews. Experts
explained that the freight price is calculated by using the projected road freight as reference value to which some amount is added (SPERANDIO ET AL., 2012). BIRKHAN (2012) estimated a 20% premium of the rail freight rate relating to the truck freight price. FRANÇA (2012) and SPERANDIO ET AL.
(2012) instanced margins of four to seven percent. A different picture was drawn by ROGÉRIO
(2012). He stated that rail transportation within the state of São Paulo is decisively cheaper than
60
Chapter 4
transportation by truck. Various rail networks converge in this state, i.e., the market and the rail
freight prices are more competitive. ROGÉRIO exemplified that rail freight would level approximately 40 % of the road freight price (e.g.: 38 R$/t railroad vs. 98 R$/t road).
Warehousing costs
In the soybean business it is not common to charge a warehousing fee. JUNQUEIRA (2012) explained that the value from farm gate to finishing the soy product is aggregated in the value
chain. Warehousing costs are absorbed by the industry or exporter. Even if a fee is charged, this
value does not cover the real costs of warehousing. OSAKI (2012) suggested that a charged fee
ranges between 3 % and 3.5 % of the value of the stored volume. To give an impression of the
price level warehousing fees published by SIARMA (2010) are listed in appendix 35. If the good is
stored on-farm for removing it at a later date, a specific date of latest removal by the purchaser is
defined and the selling price adjusted by the warehousing costs (ANONYMOUS 4, 2012).
The major cost positions of maintaining a warehouse are depreciation as well as amortization on
warehouse and maintenance facilities and other equipments (JUNQEIRA, 2012) (see figure 4.10).
The principal limitation to investments in on-farm warehouses is the high financial requirement.
KLASENER (2012) instanced a project to build a new warehouse in Nova Mutum-MT, close to Sorriso. With planned capacities of approximately 9,000 t in silo storing systems and elevators with a
performance of 100 t/h2, investment costs come by four million R$.
Figure 4.10:
Exemplary cost structure for operating a warehouse
Taxes
3%
Firewood
12 %
Electric energy
10 %
40 %
Depreciation & amortization
27 %
8%
Personnel costs
Maintenance
Source: Created by author. Based on JUNQUEIRA (2012).
2
Antique elevators of the referred warehouses had a performance of 60 h/t (KLASENER, 2012).
Chapter 4
61
ANONYMOUS 5 (2012) estimated that the storage cost in the warehouses of the trading company
for which he worked (volume of soybeans traded ≥ 2 mmt/year) average 16 R$/t. The value includes costs of classification, cleaning and drying. Information given by FREITAS (2012) revealed
that warehousing costs at COMIGO in Rio Verde are at a higher cost level of approximately
20 R$/t with 60 % of it being attributed to drying. All experts concordantly reported it as the major cost position within the operational processes. Intensity of drying and hence the costs of drying are determined by the humidity level of the soybeans at harvesting.
Farm gate prices
The price, which the Brazilian producer receives at the local warehouse, is represented by the
locally quoted spot market price. Brazilian soybean prices are closely related to the CBOT quotation (see appendix 2) and accompany world market developments (illustrated in appendix 36).
Reportedly, any price movement in Chicago is transferred to the Brazilian producer prices (see
chapter 1). The local spot market prices tend to be lowest during peak harvest time (February to
April) and to increase afterwards (July to October) (see appendix 37). When stocks get smaller
and supply on the global market is scarce in the interim period of Brazilian and U.S. soybean harvest, world market prices tend to increase until the U.S. soybean harvest season begins in late
September (see appendix 36). USDA FAS (2012) asserted that soybean world market prices are
trending lower when the new U.S. crop enters the market, which is encouraging the liquidation
of Brazilian stocks until that date.
Other costs
Brokers and other intermediaries participating in the export process generally charge a fee for
their services. CEPEA indicated a default value of 7.50 US$/t, which aggregates all related costs in
the position commissions and brokerage.
Experts often mentioned a unquantifiable cost, called custo Brazil. It describes the costs arising
by inefficiencies in the system, which diminish the international competitiveness of Brazilian
goods (KUSSANO/BATALHA, 2009, p. 29). AMARAL (2012) instanced long and complicated decision
paths in institutional processes that inhibit process flows and progress of projects. ANONYMOUS 4
(2012) added that the complex fiscal process also plays an important role for the custo Brazil.
4.2.2
Cost reporting
The costs are analyzed in two steps. In a first step total logistics costs of the soybean export
process until the domestic seaport and their impact on the local producer price are studied in
detail. In a second step a closer look is taken on the transport costs and its composition. To examine the effects of changes in the underlying assumption, a sensitivity analysis was conducted.
62
Chapter 4
4.2.2.1
Total logistics costs
The logistics costs analysis considered the costs, which arise between farm gate and loading the
soybeans onto the ship. It was assumed that the producer delivers the soybeans to the warehouse and bears the costs of short distance transportation, for which DIAS (2012) reported costs
between 0.02 R$/tkm and 0.04 R$/tkm. However, no other data was available to validate the
values. Due to the insufficient basis of data, the analysis starts at the local warehouse. The local
spot market quotation served as benchmark for the comparison with the theoretical price. As
elucidated in chapter 4.2.1, storage costs were neglected. The related taxes ICMS and COFINS/PIS3 equal 0.00 R$/t (position 4.3 in table 4.5) (see chapter 4.2.1).
Table 4.5:
Logistics cost calculation for selected regions (April 2011)
APRIL 2011
Origin
Distance to Santos
Sorriso-MT
2,200 km
Rondonópolis-MT
1,400 km
Rio Verde-GO
1,050 km
Barretos-SP
500 km
198.22
145.60
120.75
60.75
1. FOB Santos
2. Exchange rate
US$/t
R$/US$
515.95
1.59
4. Export expenses
4.1 Port fee
4.3 Taxes
4.4 Damage
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
818.53
31.86
15.86
11.90
0.00
4.09
6. Freight rate
R$/t
R$/t
786.67
8. Price at warehouse
R$/t
R$/t
R$/t
230.08
588.45
599.17
177.46
641.07
655.17
152.61
665.92
669.67
92.61
725.92
730.50
Difference [9]-[8]
R$/t
%
%
10.71
1.82
86
14.09
2.20
82
3.74
0.56
79
4.58
0.63
66
Share [6]/[7]
In the exemplary calculation for April 2011 (see table 4.5), it is 28 % of the turnover in Santos
(position 5) that has to be spent on logistics for soybean exports from Sorriso. This percentage
value decreased the shorter the distance travelled (Rondonópolis: 22 %; Rio Verde: 19 %, Barretos: 11 %), or if freight prices tend to be lower (see exemplary calculation September 2011 in appendix 38). This might result in reduced competitiveness of the remote regions of Mato Grosso in
terms of prices received. When considering that agricultural input prices like the fuel prices (see
appendix 39) are higher in these regions, it becomes clear that the previously stated competitive
3
COFINS/PIS is a social contribution tax, charged by the federal government based on the gross revenue.
Chapter 4
63
advantage of the Center West farming from low production costs (see appendix 1), can be largely
offset by the high prices for logistics services and its impact on local soybean producer prices and
agricultural input prices.
The major cost position of the total logistics costs is the cost for transportation, represented by
the freight rate (position 6). It is strongly related to the distance travelled, i.e., it increases proportionally while total logistics costs per tkm decrease (see table 4.6). While in the case of Barretos, which is situated closest to Santos port, transport costs accounted for 66 % of the total logistics costs in April 2011 and represented 0.19 R$/tkm, this share leveled 86 % in the case of Sorriso but only 0.10 R$/tkm. Export expenses (position 6) constitute the difference value.
Table 4.6:
Total logistics costs - comparison of selected regions (R$/tkm)
Sorriso-MT
2,200 km
Rondonópolis-MT
1,400 km
Rio Verde-GO
1,050 km
Barretos-SP
500 km
0.1453
0.1345
0.1852
0.1916
R$/tkm
April 2011
September 2011
0.1046
0.1000
0.1268
0.1128
Various factors might be influencing the spot market prices and cause differences between the
local spot market price and the calculated theoretical price, as shown in figure 4.11. On the one
hand, market power could be a reason for differing values. The theoretical price includes only
visible cost factors. However, the power of demand and supply may act as a price driver for local
spot market prices. It may increase local spot market prices in periods of low supply or high world
market prices, or lower the prices in a contrasting market situation, respectively. A further factor
of influence might be time. To move the soybeans from the local warehouse to the ship it takes
one day or more (see table 4.2). The transit time represents a time shift. The local spot market
price is quoted at trading day x while the fob price received may refer to a later date, at which
the product is loaded onto the ship (fob price quotation at day x+n, where n = the transit time in
days). As the market is volatile, changes might have occurred within this period, so that calculated prices may vary from the local spot market prices. Besides, it has to be considered that the
used values are monthly averaged values, which might lack precision and congruence.
The observed differences were highest in Rondonópolis. As its travel distance to the seaport is
not the largest of all selected regions but Rondonópolis is a major trading place, it could be assumed that market behavior is the major determinant in this case. The calculation performed for
the month of September in 2011 revealed that the differences in prices increased between April
2011 and September 2011 for the Center West regions of Sorriso, Rondonópolis and Rio Verde.
Freight prices were lower (Sorriso and Rondonópolis: 7 % decrease; Rio Verde: 12 % decrease) in
September 2011 and producer prices higher (Sorriso: 15 % increase; Rondonópolis and Rio Verde:
64
Chapter 4
13 % increase) so that the share of transportation costs on overall logistics costs slightly decreased. In Barretos freight prices remained on an equal level (0 % change) and the relation of
transport costs to total costs did not change significantly. This could be reasoned by the low importance of the soybean crop for that region and a probably continuous demand for transportation of agricultural products over the observed period. As a result, freight prices do not vary
much before, during and after the soybean harvest season as it is the case in the other regions.
The local spot market price increased less (8 % increase) than in the other selected regions. The
higher increases could be attributed to the decreased freight rates. A relation between freight
price decrease and producer price increase could be assumed.
Figure 4.11:
Comparison of theoretical and real producer price
(April 2011, September 2011)
April 2011
R$/t
R$/t
September 2011
R$/t
800
240
800
780
220
780
R$/t
0%
240
220
760
760
200
200
2%
740
740
1%
180
720
700
680
2%
180
160
700
160
140
680
140
120
660
1%
660
5%
720
4%
120
640
640
100
100
620
620
80
600
580
2%
560
Barretos
Rio Verde Rondonópolis Sorriso
Theoretical Price
Producer Price
80
600
60
580
60
40
560
40
Barretos
Freight rate (2nd axis)
Rio Verde Rondonópolis Sorriso
Total logistics costs (2nd axis)
Figure 1. Comparison of theoretical and real producer price (April 2011, September 2011).
4.2.2.2
Cost of truck transport
Based on the information given by the transport service providers a purchase price of
400,000 R$/truck and an asset depreciation range of five years for the truck were assumed
(ANONYMOUS 4, 2012; PARREIRA, 2012; ANTUNES, 2012) (see table 4.7). An annual travel distance of
80,000 km was taken as reference value for the calculation of fixed costs. The period of effective
use of the truck in the company depends on its maintenance costs. It is replaced at latest, when
maintenance costs begin to increase or manufacturer's warranty expires. The applied interest
Chapter 4
65
rate on capital was 2.5 % and might be attributed to subsidies. The low interest is an incentive for
high annual depreciation rates (PARREIRA, 2012). The monthly paid gross driver labor rate varied
from 2,640 R$/month to 4,600 R$/month. It was reasoned that self-employed truck drivers, who
are largely unregulated (USITC, 2012, p. 3-5), might calculate a lower labor rate than the transportation companies that may pay higher salaries. Hence, a medium value of 3,500 R$/month, as
indicated by ANTUNES (2012), was adopted. Maintenance costs were reported with a fixed monthly amount of 5,000 R$, which includes costs relating to tires.
Table 4.7:
Fixed costs of model
Average fixed capital
Interest on capital
Depreciation
Maintenance costs (incl. tires)
Gross driver labor rate
Truck insurance
Truck licence
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
300,000
7,500
40,000
60,000
42,000
8,000
1,300
Total fixed costs per truck and year
Total fixed costs per km
R$
R$/km
158,800
1.99
Total fixed costs per tkm
R$/tkm
0.0536
The analysis revealed fixed costs Cf of 1.99 R$/km or 5.36 Brazilian centavos/tkm. The two major
cost elements of fixed costs were identified to be the costs for maintenance (38 % of Cf) and the
gross driver labor rate (26 % of Cf ) (see table 4.7).
Total transportation costs C were strongly differing between the case study regions. A large difference in transportation costs of 71 % was observed when comparing the production area closest to Santos port (Barretos, 500 km: 4.18 R$/km and 0.11 R$/tkm) and the most distant production area (Sorriso, 2,100 km4: 3.42 R$/km and 0.09 R$/tkm) with the data of March 2011. Figure 4.12 illustrates the disparities of transport costs between the analyzed soybean production
areas in Brazil. It further shows the allocation of variable costs cv and fixed costs cf as well as the
total transportation costs depending on the travelled distance.
4
In this figure the Sorriso-Santos route via Goiás (2,100 km) was considered, while other routes from Sorriso to Santos
have averaged distances of 2,200 km.
66
Chapter 4
Figure 4.12:
R$/km
7
Costs of road transportation (March 2011)
32%
47%
R$
8 000
71%
Total costs (R$)
6
5
4.18
4
3
3.42
1.44
3.52
1.53
3.61
Variable costs
6 000
Fixed costs
5 000
4 000
1.63
2.19
3 000
2
1
7 000
2 000
1.99
1.99
1.99
1.99
Sorriso
Rondonópolis
Rio Verde
Barretos
0
1 000
0
The variable costs per km decreased the larger the distance travelled. In contrast, total costs per
route increased decisively with the distance travelled, which might heavily affect the competitiveness of soybeans from more distant regions. Except for Barretos (500 km), cf exceeded cv per
truck transport due to the long distances travelled in any other case. The share of Cf over C varied
from 48 % (Barretos) to 55 % (Rio Verde) and 57 % (Rondonópolis, Sorriso). Variable costs per km
cv/km impacted most in the case of Barretos, where cv/km were 35 % above those of Rio Verde.
In the other cases, cv/km differed more slightly by six percent one from another.
The costs of truck transport were analyzed for the year 2011 at the starting of harvest (March), at
peak harvest time (April) and at the ending of the export season (September). As toll costs remained constant over the year 2011, variations in fuel prices determined the changes in total
transport costs. As the Brazilian fuel industry is located close to the eastern coastline, the diesel
has to be shipped to the production regions. The local fuel price increases in consequence according to the distance due to elevated acquisition costs (see appendix 39). Within the time
scope of the analysis (export season 2011), fuel prices were at Sorriso highest in September 2011.
The total costs for truck transport from Sorriso to Santos differed between March and April
14.70 R$/km (0.40 R$/tkm), and between March and September by 11.55 R$/km (0.31 R$/tkm).
In the other regions fuel prices remained constant or slightly decreased. The absolute cost difference equaled at Barretos 1.00 R$/km (0.03 R$/tkm; March/April) and 1.50 R$/km (0.04 R$/km;
March/September) with a change of 0.4 % in the local diesel price. A 1.1 % (March/April) and a
1.5 % (March/September) change in diesel prices at Rio Verde reduced costs by 12.08 R$/km
(0.33 R$/tkm) and 16.28 R$/km (0.44 R$/tkm), respectively. In Rondonópolis the diesel prices
continued on the same level from March to May 2011 so there was a zero effect in transport
costs in this period but a slight effect of 4.50 R$/km (0.12 R$/tkm) between September to March.
In the cases under consideration, toll costs were charged on privatized highways in São Paulo.
The impact of this cost position increased the shorter the distance. While toll costs accounted in
Chapter 4
67
the case of Sorriso for eight percent and in Rondonópolis for twelve percent of the total transport
costs, the share was with 16 % decisively larger for Rio Verde. For transports from Barretos toll
costs were a major cost positions, which held in the exemplary calculation a share of 29 %. The
drivers often cut tolled streets in order to save expenses (PARREIRA, 2012). This might imply the
use of roads in worse conditions and result in elevated costs of fuel or losses (see chapter 5.1).
To elucidate the cost impact of toll costs on total costs a supplementary calculation was performed for the alternative route Sorriso-MS-Santos (route I in appendix 30). The choice of this
route implies a 21 % increase in toll costs compared to route two (Sorriso-GO-Santos). The higher
costs for tolls combined with an increase in travel distance of 100 km lead to a six percent increase in total transport costs relating to route two. There was no detailed data available on the
saving potential by choosing route one with respect to maintenance and fuel costs.
A comparison of the transport costs and the freight rate (represented in figure 4.13) revealed
differences, which most experts attributed to the relation of demand and supply at the different
points of time in the soybean export season. The demand for transport services increases decisively at beginning of the export season (see chapter 4.1.3), driving freight rates up. Figure 4.13
illustrates that freight prices were highest in March whereas the freight rate earned in September
did not cover transport costs in most cases. According to PARREIRA (2012), transport costs often
exceed the freight rate received on the market. The freight rates used in this analysis are averaged market values. The costs of truck transportation are averaged costs of transportation companies, which do not include self-employed drivers. However, the market freight price integrates
the freight prices of the self-employed drivers. As vehicles of self-employed truck drivers are often older (see chapter 2.3.6) costs for depreciation and maintenance might vary, costs of labor
might be calculated differently and fixed costs are lower. It should be therefore considered that
costs might differ distinctly between the different market participants. According to USITC (2012,
p. 3-5), fierce competition among the self-employed truckers, hence, often drives down the price
of road transport services below the total transport cost of the transportation companies.
Figure 4.13:
Comparison of transport costs and freight rates
R$/t
Sorriso transport costs
Sorriso freight rate
220
200
180
+10%
+2%
160
-5%
+9%
Rondonópolis transport costs
Rondonópolis freight rate
140
120
100
+23%
+3%
+7%
+12%
-2%
+8%
+8%
+8%
March 2011
April 2011
September 2011
80
Rio Verde transport costs
Rio Verde freight rate
60
40
Figure 1. Comparison of transport costs and freight rates.
Barretos transport costs
Barretos freight rate
68
Chapter 4
4.2.2.3
Sensitivity analysis
A sensitivity analysis based on the data of March 2011 was conducted in order to examine the
effects on transportation costs if an underlying assumption changes.
As explained before, fixed costs may vary according to the age of the truck. First, it was examined
a ten percent variation of maintenance costs(see figure 4.14). Maintenance costs generally increase with the age of the vehicle or with a more frequent driving on roads in worse condition
(assumed cost increase: +10 %). In the opposite case, a newer truck or driving on better roads
might reduce cost compared to the reference case of chapter 4.2.2.2 (-10 %). The ten percent
variation of maintenance costs resulted in a variation of four percent of fixed costs and two percent of total transport costs. The ten percent cost reduction represented saving potentials of
0.08 R$/km (0.002 R$/tkm) in fixed costs. As fixed costs are not tight to the distance travelled,
any change will result in the variation of total costs at the same percentage in each studied region. In this analysis, the change leveled two percent (minimumBarretos V: 1.80 %; maximumSorriso I:
2.24 %), which represented an absolute variation in total transport costs of maximum 165.00 R$
for route one and of minimum 37.50 R$ for route five.
Figure 4.14:
R$/tkm
0,120
0,115
0,110
0,105
+2%
0,100
0,095
-2%
0,090
0,085
Sorriso
I
Basis
Sorriso Rondonópolis Rio Verde
II
III
IV
Maintenance +10%
Barretos
V
Maintenance -10%
Figure 1. Sensitivity analysis - maintenance costs.
Source: Created by Source:
author Created by author.
Figure 4.15 represents the factor variation of the interest rate level. While the interest rate at the
low level of 2.5 % was considered for the basic calculations, an increase of the interest level was
assumed. In the case of i = 5 %, total transport increased by two percent (route five) or three percent (route one to four) which represented additional transport costs of minimum 46.88 R$ in
case of the shortest distance travelled (route five) and of maximum 206.25 R$ in case of the largest distance travelled (route one). At i = 7 % the change in transport costs for route five represented four percent (+84.38 R$), while the impact on route one was five percent (+371.25 R$).
The analysis showed that an increase of the interest rate implies considerably increased transport
costs per ton, diminishing the profit margin or increasing losses if the freight rate is below
Chapter 4
69
transport costs. The low interest rate of 2.5 % could be an appropriate measure to keep freight
prices low and give incentives to invest in new equipment. Any increase of the interest rate and
corresponding increases in transport costs can provoke losses of competitiveness of the soybean
producers in regions far from the seaport.
Figure 4.15:
Sensitivity analysis - interest rate
R$/tkm
0,120
0,115
0,110
0,105
0,100
0,095
0,090
Sorriso
I
Sorriso Rondonópolis Rio Verde
II
III
IV
Basis (i = 2.5%)
i = 5%
Barretos
V
i = 6%
i = 7%
Figure 1. Sensitivity analysis - interest rate.
It was furthermore performed a sensitivity analysis by varying the fuel costs by five, ten and fifteen percent. Appendix 39 shows that the local fuel prices increased since 2004 by about 40 %
until 2011. As fuel costs are reportedly one of the major elements of the freight composition (see
chapter 4.2.1), the freight rate probably accompanies increases in fuel prices. The sensitivity
analyses confirmed this assumption and revealed that a change in the level of fuel costs is reflected in a proportional increase of total transport costs, as illustrated in figure 4.16. For a
change of five percent in fuel prices, absolute transportation costs represented the values
128.04 R$ (route one), 122.22 R$ (route two), 78.23 R$ (route three), 54.63 R$ (route four) and
24.66 R$ (route five).
These values doubled with ten percent increase and tripled with a fifteen percent increase in fuel
prices. This means that additional costs are passed onto the producers with the effect of diminishing the price received by the producer for the soybeans.
In any case, it was observed that the percentage change of total transport costs decreased with
the distance. It showed that the effect of changing a factor is magnified by the distance. Any variation resulting in lower transport costs would benefit the producers in the Center West more
than producers of production regions closer to the export port. Likewise, any cost increasing factor variation will harm the former more than the latter.
70
Chapter 4
Figure 4.16:
Sensitivity analysis - fuel price
R$/tkm
0,120
0,115
+4% +2%
0,110
+1%
0,105
0,100
+5%
0,095
+3%
0,090
+2%
0,085
Sorriso
I
Sorriso
II
Rondonópolis Rio Verde
III
IV
Basis (March 2011)
+5%
+10%
Barretos
V
+15%
Figure 1. Sensitivity analysis - fuel price.
4.3
Impact assessment of soybean transports on climate change and interpretation
The field data provided first-hand information for the GHG inventory model and enabled to draw
an exemplary picture of soybean transportation processes within the Sorriso-Santos corridor. The
observed CO2 fluxes ranged between 18.92 kgCO2eq/t (500 km) and 83.24 kgCO2eq/t (2,200 km).
GHG emissions from road and intermodal transportation were calculated for transport processes
from each of the selected region. Figure 4.17 illustrates the impact of the distance on GHG emissions. Setting the shipping from the most distant region (Sorriso) into relation to the region,
which is situated closest to Santos port (Barretos), revealed an increase in emission volume corresponding to the higher energy demand for longer distances. The emission quantity of the Sorriso-Santos route (83.24 kgCO2eq/t) is 4.40 times as high as the emissions resulting during travelling
from Barretos (18.92 kgCO2eq/t) to Santos. The climatic impact caused by the exemplary soybean
export processes for each analyzed region is represented in figure 4.18. The results give evidence
of a reduced efficiency of soybeans produced in the Center West compared to soybeans produced closer to the export port. From this point of view Sorriso is least efficient and Barretos
most efficient as a soybean production region in terms of carbon emissions from transportation.
The emission based evaluation of the climate impact of both road and intermodal transports of
rail and road shipping revealed that road transportation is the more damaging modality of the
options. Due to a lower fuel combustion per tkm, a train emits only half (0.02 kgCO2eq/t) of the
GHG emissions that a truck emits (0.04 kgCO2eq/t). This confirmed the results presented by SILVA
ET AL. (2010) and BIAGGIONI/BOVOLENTA (2010).
Chapter 4
Figure 4.17:
71
GHG emissions through road transport over selected distances (kgCO2eq/t)
kg CO2eq /t
100
337 %
90
80
179 %
70
111 %
60
50
83
40
30
53
40
20
10
19
0
Barretos
Rio Verde
Rondonópolis
Sorriso
The evaluation demonstrated that intermodality is more efficient if road transports to the rail
terminal are over long distances (see case Sorriso). For the case of Rondonópolis, the competitive
advantage of intermodality over road transports is only slightly distinct. This may be explained by
the need to additionally consider 70 % of the gases (see chapter 3.6) that are emitted during the
return travel to the terminal of Alto Araguaia. If considering that the train returns fully loaded
with other products, emissions would be attributed to these other product process chains instead of the soybean export process. This would result in a mitigation of the GWP of soybean
exports by 24 % (13.72 kgCO2eq/t) compared to the previous assumption, or, by 31 %
(25.53 kgCO2eq/t) for the distance Sorriso-Santos (800 km road plus 1,400 km railroad) and 34 %
(17.96 kgCO2eq/t) for the distance Rondonópolis-Santos (200 km road + 1,400 km railroad).
In a sensitivity analysis, fuel efficiency was varied to examine the reduction potential for climate
effects. In a first step, it was assumed that diesel quality will improve. The application of the
emission factor of 2.8 was explained in chapter 3.6 with the low diesel quality utilized in Brazil,
which was assumed to improve. The applied emission factor (2.8) was adjusted towards the IPCC
default value (2.67). The variation showed that by consuming higher quality diesel GWP can be
reduced up to five percent. The results are represented by the black marks in figure 4.18.
72
Chapter 4
Figure 4.18:
GHG emissions during soybean exportation for selected transportation routes
and modalities
kg CO2eq /t
100
90
80
70
60
50
40
30
20
10
0
Road
83.24
Intermodal
71.43
Intermodal
one-way
57.71
52.97
5 % variation
of emission
factor
48.73
39.73
35.01
18.92
Sorriso-MT
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
In a second step, a variation by ten percent of the fuel consumption of a truck was assumed. The
target of Brazil to reduce the average age of the truck fleet might have a positive impact on fuel
efficiency and thus on exhaust emissions (see appendix 40). As GHG emissions are tight to the
fuel combustions, the variation of a truck's fuel consumption by ten percent resulted in a proportional reduction or increase in GHG emissions if soybeans are shipped only via road. The intermodal option displayed a more moderate change in emissions. In this case, a ten percent reduction in the trucks' fuel consumption reduced the GHG emissions by six percent for the SorrisoSantos route and by four percent for the Rondonópolis-Santos route, respectively. The results
shown in figure 4.19 give evidence that the intermodal option continues to be more efficient for
transports from Sorriso to Santos. However, with an improved fuel efficiency road transport becomes more efficient when transports start in Rondonópolis. The breakeven point between road
and intermodal transportation was calculated at a truck's fuel consumption of 0.46 l/km (-8 %).
For the Sorriso-Santos route a decrease of fuel combustion per km to 0.39 l/km (-22 %) would
equal the emission volume of road and intermodal transportation. In the other case of fuelefficiency losses, e.g., due to worsened road conditions or a bad driving manner of the trucker,
emissions increased by ten percent, proportionally to the assumed change in fuel consumption.
Chapter 4
Figure 4.19:
73
Sensitivity analysis - fuel efficiency
kg CO2eq /t
100
Road (Increased
fuel consumption)
Road (Basis)
Road (Reduced
fuel consumption)
Intermodal (Increased
fuel consumption)
Intermodal (Basis)
Intermodal (Reduced
fuel consumption)
90
80
70
60
50
40
30
20
10
0
Sorriso-MT
(2 200 km)
Rondonópolis-MT
(1 400 km)
Rio Verde-GO
(1 050 km)
Barretos-SP
(500 km)
For road transportation the principal factor of influence was the fuel efficiency of trucks, which
represented the highest potential for GHG mitigation. An improved emission factor was the best
option for intermodal transportation. However, domestic transports accounted for a much higher
amount of GHG emissions compared to ocean freight shipping. According to the IEA (2009), a
bulk carrier operating on the sea emits even in the worse case less than 10 kgCO2eq/tkm (see appendix 41), which leads to the assumption that bulk carriers operating on the inland waterways
correspondingly emit decisively less emissions than the road transport mode.
Chapter 5
5
75
A well functioning logistics network is a decisive factor to induce and facilitate competitiveness
and development of the soybean sector. Brazil's current infrastructural system is underdeveloped
relating to its continuously growing output of agricultural and industrial products. Governmental
programs like the PNLT (see chapter 2.3.5) target a more balanced transport matrix with a higher
participation of rail and water transportation modalities.
This chapter shall illustrate the bottlenecks and challenges of the Brazilian transport sector and
give an outlook of the potential development of soybean logistics in Brazil.
5.1
Road transportation
ANONYMOUS 3 (2012) asserted that large parts of the highway infrastructure are antique. Roads
were constructed in the 1970s and subsequently only maintained by clumsy mending and closing
holes. Single lanes per direction of traffic cause congestion and dangerous overhauling
maneuvers put traffic participants at risk (ANONYMOUS 4, 2012). CNT (2011, pp. 277, 286) reported
that the condition of the road directly influences diesel consumption and losses of cargo. Worse
road conditions force the driver to drive slower than optimum speed and more diesel is consumed. ANONYMOUS 4 (2012) reported an elevated level of losses at roads in worse conditions,
which companies generally calculate with of 0.5 % of the total volume per year (AMARAL, 2012;
ANTUNES, 2012; JESUS, 2012). Applying this number to the total export volume of Brazilian soybeans
(2011: 32.99 mmt) reveals annual losses of 164,950 t of soybeans. Assuming a fob price of
524.69 R$/t (averaged fob price at Santos over the period of February to September 2011) Brazil
lost in the 2011 export season 86.55 million R$ because of volume losses during transportation
processes.
One major project is the pavement of the BR-163 from Sinop-MT to Santarém-PA (ANONYMOUS 3,
2012) (see appendix 42). Its completion was planned for 2008 (VERA-DIAZ/KAUFMANN/ NEPSTAD,
2009, p. 3) but to date there still remain 300 km to be paved (BIRKHAN, 2012). This unpaved part
prejudices the export process to Santarém as during the rainy harvest season the track become
impassable for the heavy-duty trucks (FRANÇA, 2012). VERA-DIAZ/ KAUFMANN/NEPSTAD (2009) revealed that paving the BR-163 to Santarém and the rerouting of soybean exports to the Santarém
port could reduce the transit time and lower the cost of shipping by averaged 10 US$/t (20 R$/t)
for soybeans produced in the northern part of Mato Grosso. USITC (2012, p. 3-5) reported a value
of 20 US$/t (41 R$/t), while BIRKHAN (2012) estimated an impact on shipping costs by up to
30 US$/t (61 R$/t) and increasing local spot market prices.
The highways in the regions of Sorriso and Rondonópolis were in regular or good conditions while
the road in the municipalities bordering to the major transshipment stations of the Center West
in Alto Araguaia-MT and Alto Taquari-MT, were found in bad conditions (see appendix 33). Big
76
Chapter 5
holes in the pavement and bad surface of the pavement force trucks to drive slowly and lead to
losses of cargo. The highways are steadily restored but suffer from the high volume of the heavyduty transports. Investments in road refurbishing are apparently barely sufficient. Roads may
have a good status at the beginning of the harvest season but deteriorate quickly during the
harvesting periods of the soybeans and corn. In the region of Rio Verde, large road tracks were
under construction. ANONYMOUS 5 (2012) stated that road conditions were poor until 2010 but
improved decisively since that time by renovation and duplication. GARCIA DA SILVA (2012) pointed
out that travel time has decreased decisively at certain tracks. He instanced that the travel time
on the export route in direction to Santos from Rio Verde via the BR-452 Itumbiara-GO
diminished from eight hours to three hours due to improvement of the infrastructure.
5.2
Rail transportation
High freight prices and line-ups at the gates of the rail terminals reflect the bottlenecks of the rail
system (KLASENER, 2012). The high prices are not only a result of monopolistic price setting but are
also influenced by infrastructural deficiencies and administrative bottlenecks (MONTEIRO/SEBBEN/GOLIN, 2011). The capacities of rail equipment and terminal installations of the rail
terminal in Alto Araguaia are not sufficient to cover the demand for transportation services in the
designated time schedule (SPERANDIO ET AL., 2012). In the context of the PNLT (see chapter 2.3.5), a
new rail network, called “agribusiness railroads” by VENCOVSKY (2011), is being implanted in
planned corridors (see appendix 43) that are designed to link production and consumption regions (CASTILLO/VENCOVSKY/BRAGA, 2011, p. 20). A new rail terminal was established in June 2012 in
Itiquira-MT (distance to Alto Araguaia: 120 km) (FERREIRA, 2012) and a major terminal in Rondonópolis (distance to Alto Araguaia: 250 km) is planned to be inaugurated in 2013. It is expected
that the completion of the project improves intermodal access to the Port of Santos for a broader
range of production regions and opens up the opportunity of increasing intermodality. APROSOJA
(2012) projects that the ALLMN railroad will transport an annual volume of 15.50 mmt of soybeans and corn in 2022 (+48 % relating to 2010, see chapter 2.3.2).
While one of the major targets of the projects of the PNLT is to lower the total logistics costs and
increase efficiency, SPERANDIO ET AL. (2012) and FRANÇA (2012) estimated that the extended railroad to Rondonópolis would not change the freight price. They instanced the case of the rail terminal of Alto Taquari-MT and Itiquira-MT, where freight rates did not drop. SPERANDIO ET AL. (2012)
pointed out that expectations of lower transport costs and better access to Santos benefitting
the regional producers by lower freight rates failed. In reality, the terminal benefitted only the
big trading companies while even prejudicing the producers by high traffic densities causing road
deterioration and heavy congestion. Nevertheless, as the soybean yields are continuously increasing, more freight will be moving on the highways. Thus, the installation of the rail terminal
in Rondonópolis enables to relief the roads from imminent higher volume of traffic on the traditional export corridors by relocation large volumes from road to railroad. This might affect freight
Chapter 5
77
rates of trucking positively in the benefitting regions, as more trucks could be available for short
distance transports due to shrinking demand (ANTUNES, A. 2012).
Some experts believe that lacking competition and restricted access to rail transport services will
mitigate any cost advantages of the extended railroad. Any change in rail freight price changes
will depend on the structure of the rail terminal, i.e., if the monopolistic structure of rail operations will continue, if volumes turned over will explore the handling capacities at maximum and if
equipment and installations will enable a more competitive price due to low operational costs
(FRANÇA, 2012; BIRKHAN, 2012; ). Thus, the effect of the railroad expansion on logistics costs remains uncertain.
Rail freight prices that do not necessarily offer a cost advantage compared to road freight rates, a
faster transit time of trucks and the avoidance of waiting times at the rail terminal increase the
attractiveness of road transportation for traders. FRANÇA (2012) instanced that the trading
company for which he worked (volume of soybeans traded ≥ 2 mmt/year) operated an own rail
terminal in Alto Araguaia until 2011, which was closed due to the mentioned reasons. In order to
improve the efficiency of the rail network the government reviewed in 2011 its concession model
and implemented a new rail regulation (RESOLUTION N°3.696/11). This asserted that any idle capacity of a rail section must be available to other operators in order to perform the right of way
or mutual traffic (ANTT, 2011). The USDA (2011) forecasted a positive impact of the law on grain
and soybean exports by facilitating the marketing of agricultural products. Even though, the
USITC (2012, pp. 3-09) estimated that despite railroad utilization might be more efficient, rail
prices won't decrease due to limited capacities and limited competition.
5.3
The highest potential of relieving the port of Santos of the great handling volumes and the Sorriso-Santos export corridor from the high traffic density is estimated to be in the development of
the waterway system in the northern states (see appendix 44). PERRUPATO (2011) estimated a potential of 15,000 km for extending the network of waterways, with most of it being located in the
North. BIRKHAN (2012) accredited the greatest potential for decreasing logistics costs for soybean
exports from the Center West to the development of the Tapajós waterway. ANTAQ (2012, p. 16)
confirmed this, projecting that, after developing the waterway and connecting it to Mato Grosso
via the BR-163, the so called Sorriso-Santarém route, will become the principal export corridor
for soybeans produces in central and north Mato Grosso.
5.4
Storage capacities
The reportedly lacking storage capacities (see chapter 2.3.4) force producers to market the soybeans before the corn safrinha harvest starts. These deficits in storing capacities result in logistic
78
Chapter 5
problems like congestion at farms, warehouses and ports during peak harvest season. Long lineups at the warehouses in the production regions and at the gates of Santos port are a common
picture during the peak of soybean exports in February, March and April (KLASENER, 2012). JESUS
(2012) added that trucks are utilized as warehouses on wheels in order to overcome the scarcity,
which in turn has a price driving effect when demand increases and truck capacities are getting
scarce. According to EIU (2010, p. 11) insufficient storage space result in monetary losses of averaged one US$ per bushel of soybeans because producers are forced to sell at harvest time when
prices are at the lowest level. Depending on the market conditions, this loss can be higher.
KLASENER (2012) underlined that the decision to store the soybeans in order to benefit from higher
prices in the period between the Brazilian and the U.S. harvest is an exceptional case even
amongst large producers. Only highly capitalized producers, who are not dependent on the revenues for financing the inputs the next season, can afford storage over a longer period of time.
Table 5.1:
Medium waiting time at port terminals (h) in Santos (2009, 2010).
Port terminal
2009
2010
ADM (Corex)
TGG
Cargill
133
78
77
213
124
60
Source: ANTAQ (2011a, p98).
The great need for strategic situated warehouses in rural production became apparent in the
2012 season, when optimum climate conditions during planting and grain fill stages as well as
expanded planting area rendered a high output of the corn safrinha harvest. This resulted in a
high demand for storage capacities. Consequentially, soybeans had to be rapidly sold to clear the
warehouses in order to be able to take in the corn crop when freshly harvested. 86 % of the produced soybeans were marketed until the end of April 2012 (IMEA, 2012). Even though, large volumes of corn had to be stored on the field or open air, respectively, due to lacking storage capacities, raising the risk of losses due to spoilage and mold caused by warm temperatures and moist
climate (no data on losses available) (KLASENER, 2012).
5.5
Port system
Experts agreed that the major bottleneck of the soybean export logistics is the deficient port infrastructure. The lack of sheltered ship loading space was recorded as one of the most important
weaknesses as breakdowns during ship loading due to adverse weather conditions occur lead to
long waiting times (see table 5.1). BIRKHAN (2012) calls for a replacement of prevailing precarious
technical port installations and antique machines while ANONYMOUS 3 (2012) outlined the need for
modernization of the port communication in Santos. Even though the time-limited concessions
for running a port terminal might discourage long-term investments (ANONYMOUS 2, 2012), many
concessionaires engage in developing new or improving existent port infrastructure.
Chapter 5
79
ANONYMOUS 3 (2012) explained that the trading company for which he worked (volume of soybeans traded ≥ 2 mmt/year) invested in a recently implemented integrated information system,
which works as scheduling system for reception. If ship loading is delayed or capacities are not
fully explored the system transfers this information to the logistics department that immediately
reduces or increases the volume sent to the seaport. According to ANONYMOUS 4 (2012) the effect
was a 98 % reduction of the waiting time at the company's terminal.
The PNLT initiated projects for improving port infrastructure and access to land. UK TRADE AND
INVESTMENTS (2011, pp. 10) estimated that the PNLT investments in infrastructure and port logistics will enable to increase the handling capacity of Brazil's ports up to 30 %.
Trading companies focus their investments in port infrastructure in northern Brazil. One large
trading company, e.g., invests in port terminals in Santarém-PA where the company aims to double the storage capacities by 60,000 t (FRANÇA, 2012; ANONYMOUS 5, 2012). APROSOJA (2011) estimated an increase in mass flows to Santarém of 122 % and predicted the volumes exported via
São Luiz to increase by 462 %. These ports are estimated to cover the major part of the projected
volume increase during the next decade. Appendix 45 illustrates the projected development of
soybean exports and shows the projected decrease of the area of influence of Santos port. The
concentration of companies' investments in infrastructure development of the northern ports as
stated by AMARAL (2012) and FRANÇA (2012) reflects the importance that is accredited to the
northern ports of São Luiz-MA and Santarém-PA. However, difficult institutional processes for
license granting and environmental protection measures required decelerate and hamper speedy
completing of the project. Problems to obtain operational licenses due to environmental activism
and bureaucracy impede a quick development (FRANÇA, 2012).
5.6
Truck drivers law
In September 2012, a new law (LEI DOS MOTORISTAS N°12.619), called the truck drivers law, was put
into effect (PORTAL BRASIL, 2012). It regulates the working time of truck drivers, limiting the maximum permitted working time to eleven hours per day including rest breaks of minimum 30
minutes every four hours wherever the truck may be driving (GODINHO, 2012; JESUS, 2012).
Most experts estimated that the law will have a significant effect on operational time and operational costs. The monthly performance per truck and driver was predicted to decrease by approximately 35 % (GODINHO, 2012; JESUS, 2012). Transport service providers expected operational costs
to increase by 30 % to 45 % in comparison to the freight rates before the law, as well as prolonged operational time (+30 %). Conservative estimates of a large trading company (volume of
soybeans traded ≥ 2 mmt/year) predicted an increase of only ten percent. There was a consensus
of the experts that costs due to supply shortages will be the major price driver. ANONYMOUS 5
(2012) assumed a 45 % increase in demand for trucks and drivers.
80
Chapter 5
Regarding the development of selected freight rates for long distance hauls during a determined
period of time (see figure 5.1), the increase of freight rates in 2012 is observable1. While the
freight rate in September 2011 was still lower than in the previous year, the 2012 freight rates
mostly surpassed the 2011 freight rates. The 2011 rates started to decrease with the ending of
the export season in August. This market behavior could also be observed in the preceding years,
whereas in 2012 freight prices began to rise in July (date of reference: July 04, 2012). The drivers
law may not be the only driving factor of the price but surely plays a key role. A prominent volume of corn harvested in the respective season kept freight prices at a high level during May to
July. At the beginning of September, the difference already achieved 30 %, where it stagnated in
Sorriso while it further increased to a 39 % difference in Rondonópolis. PRESSINOTT (2012) reported a 40 % increase in freight prices for Mato Grosso and averaged 20 % for the whole country
from January 2012 to August 2012, compared to the same period of the previous year. ROGÉRIO
(2012) outlined that in São Paulo state, where a lot of trucking companies are registered, capacities are lacking. He assigned that to the high increase in freight prices of the Center West, which
attract the haulers and lead to supply deficits in the other regions. JESUS (2012) concluded that
the price drivers are shortages in labor and the lengthened delivery time.
Figure 5.1:
Development of selected freight rates (2010-2012)
Source: Created by author. Based on data from IMEA (2012).
Experts estimate that freight rates will remain on a 30 % elevated level in the long term, while
the monthly performance per truck and driver will decrease by approximately 35 % as a result of
the law (GODINHO, 2012; JESUS, 2012). PARREIRA (2012) underlined the positive effects for the
transport service providers by considering the reduction of kilometers travelled per month per
truck and driver, which might positively impact on costs by reducing maintenance costs due to
wastage. In combination with higher freight prices PARREIRA (2012) argued that even though the
costs per driver increase, this will be offset by a higher increase in return and the savings at other
cost positions. He concluded that the distribution of costs will shift but the level of costs will not
1
Data were available only until September 2012, so that a comparison of freight rates later than September 2012 with
the same period of the previous year was not possible.
Chapter 5
81
change significantly and monthly turnover will continue equally. However, the law does not necessarily benefit the professional drivers. While prior to the law an extended working day to increase remuneration was eligible, this regulation is prejudicing particularly income opportunities
for self-employed drivers who work according to JESUS (2012) for a ten percent lower rate than a
transportation company with own fleet and employed drivers does.
Chapter 6
6
83
For being a high volume and low value-added product, high transportation costs might impact
decisively on total costs of soybean exportation. The seasonality of soybean production and the
corresponding seasonal demand for transport services is reflected in the freight price development. Given these peculiarities of the crop and considering the tight correlation to the world
market prices, it can be concluded that a principal measure to increase competitiveness of the
Brazilian soybeans should be the implementation of infrastructural programs in order to decrease
transportation costs. Brazil's challenge is the improvement of the infrastructure in the medium
term and investments to expand the network in the long term to diminish logistics and transportation costs and increase competitiveness of soybean exports from distant production areas.
Freight rates in the competitive road transportation sector are composed by the company's marginal costs plus a profit margin and are influenced by the market behavior. The price formation of
rail freight, however, is rather intransparent. The market analysis revealed that soybean shipping
via railroad from Mato Grosso, Goiás and São Paulo to Santos port is at harvest peak times more
expensive than trucking. This might be an indication that ALL, which is the only railway service
provider on this railroad, indeed exploits its customers based on a monopolistic market position.
Higher competition in the rail transport sector and the improvement of the rail infrastructure
might be an important factor to affect freight prices positively. Reforms are necessary to relocate
the transportation matrix to the high volume and low cost transport modalities of railroad and
waterway and to create more competition amongst operators.
Transport costs affect the competitiveness of soybeans produced in the inland of Brazil. The sensitivity analysis revealed that the longer the distance from production region to seaport, the larger are the effects of increased input prices. Supportive measures, such as the currently low interest rate on capital for truck financing (2.5 %, see chapter 4.2.2.2), may help keeping transport
costs at a low level and indirectly support producer prices in remote regions. Any cost increase of
input factors would result in higher transport costs and might result in losses of competitiveness
if circumstances remain equal to the reference case. Any decrease, however, offers potential to
increase the competitiveness of the soybeans. Strategic infrastructural investments link the remote agricultural areas to the infrastructure and open new export corridors to the north, like the
Sorriso-Santarém corridor (BR-163). That way, an integrative system of different modalities could
be realized. Logistics bottlenecks (see chapter 5) could be reduced. The access to more modalities
than road transportation would benefit more producers as competition could create more competitive freight rates. Redirecting export volumes from the Sorriso-Santos corridor to the SorrisoSantarém corridor could diminish congestion on the export corridor to Santos.
With the doubling of the soybean volume exported via Santos (2001: 4.60 mmt; 2011: 9.23 mmt;
MDIC, 2012) it became apparent that access to the port is lacking and capacities are not sufficient
for efficiently turning over the high volumes at low costs and small operational time (see chapter
2.2.5). Any further increase in the export volume could adversely affect the competitiveness of
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the Brazilian soybean exports if infrastructure is not improved or export volumes are not relocated to other ports. Investments in the infrastructure of the northern ports and of the northern
export corridors would enable a relocation of mass flows from the traditional corridors to the
southeastern ports, like the Sorriso-Santos corridor, to the north. This could relieve the southern
ports, where port operations are currently inefficient.
Enhanced storage capacities could also positively influence competitiveness and would help
avoiding demand excess like in the instanced case of 2012 (see chapter 5.4). Sufficient storage
capacities would reduce the need for immediate shipping at harvesting and relieve intermodal
terminal and port facilities from the high traffic volume at harvest peak. It would contribute to a
well-functioning shipping process and help to avoid costs of demurrage, to diminish transit time
and reduce transportation costs. Producers of the Center West could benefit from decreasing
freight rates if transportation costs decrease (see chapter 4.2).
While the government lacked investments during the past two decades, a change in policies within the last five to seven years was observed (see chapter 2.3.5). This development is favorable
because the great potentials to expand agricultural production in Brazil can only be efficiently
explored if the production regions are connected to the economic centers by an appropriate infrastructural network. Large parts of the infrastructural system still have to be refurbished and
continuous investments are required to keep it in a good condition. Whereas new projects were
initiated, progress is reportedly slow. The Brazil cost (see chapter 4.2.1) including bureaucratic
inertia and complicated environmental licensing hampers the quick complementation of the projects. Experts instanced that the completion of paving a 150 km road track of state or federally
administered roads in south Mato Grosso took more than 25 years due to slow institutional processes (SPERANDIO ET AL., 2012). This bottleneck was observed to be a major challenge, which
should to be overcome in the long term to improve the competitiveness of the soybean sector.
The long distance transports affect furthermore climate change. CNT (2011, p. 287) estimated
that emissions from road transport will increase by 60 % until the year 2020 (related to 2009).
With regard to the projections in chapter 2.1, it can be expected that the soybean business will
increase its demand for transportation services decisively and contribute to the projected increase in GHG emissions. The expansion of the soybean production into remote rural regions like
Sorriso resulted in a higher diesel consumption per route and a corresponding increase in GHG
emissions, when compared to transports from the traditional agricultural production areas like
Barretos. Distance is inferentially the major determinant for the volume of GHG emissions. The
analysis showed that intermodal transportation is the most efficient option at present conditions.
If fuel consumption remains on the same level or increases, the relocation of transport processes
to the railroad is recommendable from the environmental point of view. A higher fuel-efficiency
would make road transports become more competitive. If fuel consumption decreases by minimum 22 % in the case of Sorriso or eight percent in the case of Rondonópolis trucking would become even more efficient than rail. As truck technologies are already high developed, an improvement of the infrastructure is crucial for GHG mitigation in Brazil. CNT (2012, p. 358) esti-
Chapter 6
85
mated a significant reduction in fuel consumption until 2020 if road conditions will improve. If
roads remain in the conditions of the time being the emissions will more than double due to a
growing vehicle fleet and increasing demand for transportation services.
Brazil took an important step towards GHG mitigation by committing itself voluntarily to adopt
measures for mitigating GHG emissions (CNT, 2011, p. 285, IPEA, 2011, p. 33). An important
technical measure to diminish GHG emissions are fleet renewals. In economic terms, a fleet renewal might lead to increased freight prices, as costs of a new machine are clearly higher, e.g.
due to high depreciation costs, than costs of an old truck. However, the technological innovations
can help to reduce the energy demand, e.g., by reduced fuel consumption (see appendix 40).
Such technical improvements like a higher performance in fuel combustion per kilometer implies
the advance of the air quality program PROCONVE (see appendix 28). Further reductions from road
transport should be supported by policies and operational measures. Political measures may also
address the improvement in infrastructure as well as the development of vehicle technologies
and alternative energies. Operational measures include driver training for optimum driving and
an efficient logistics management. They further address the formation of environmental consciousness of the market participants. Governmental or customer incentives as well as the engagement of the company owners can foment such measures. ACEA (2010, p. 7) emphasized that
a proper logistics management helps to improve fuel and CO2 efficiency.
CORREA/RAMOS (2010) asserted that road transportation requires almost four times as much energy as water transportation and three times as much as rail transports. Concluding, a shift in the
transportation matrix from road to rail and to waterway would positively affect climate change. If
the loading factor of the return transports increases, the extension of the railroad network to
Rondonópolis offers the potential for mitigating GHG. However, the demand for soybean transports will probably continue to exceed the demand for shipments of agricultural inputs. A decrease in kgCO2eq/t might thus not be achieved. Further studies addressing the climate effects
and potentials of this soybean transports from the Center West are needed.
In conclusion, the future efficiency of the soybean export logistics depends on the integration of
the total infrastructural system, which incorporates warehouses, railroads, roads, waterways and
ports. Even though there are already a lot of initiatives and projects, it is not sufficient yet to ensure a smooth flow of goods on national level. This means that Brazil should pursue the strategy
of investing in infrastructure and focus on the realization of the. That way, maximum utilization
of capacities for minimizing costs and additional kilometers travelled could be achieved projects
and the competitive position on the world market strengthened.
The study revealed that logistics processes are determinants of the competitiveness of Brazilian
soybeans. The studied cases elucidated that logistics costs are dominated by freight prices, which
are determined by the road transport costs. This applies to the road freight rate as well as to the
rail freight rate, which in the cases under consideration was mostly tight to road freight prices.
Costs and GHG emissions are closely related to the transport distance, so that the more distant
86
Chapter 6
regions like Sorriso can decisively lose in competitiveness compared to the regions closer to the
seaport of Santos like Barretos. The need for investments was realized by the Brazilian government and many strategic projects are initiated or planned, even though, with regard to the past,
most of the projects will probably not be complemented in the planned timeframes. It is estimated that if the country keeps the current strategic focus on infrastructure, the conditions of the
infrastructural network will decisively improve in the next decade. Despite continuing infrastructural inefficiencies, Brazil will probably be a globally competitive soybean producer and exporter
due to its natural resource abundance and supportive government policies.
Chapter 7
7
Summary
87
Summary
Soybean exports in Brazil require often long distance transports. This study examined the relevance of domestic transportation and logistics processes of soybean exports in terms of costs and
CO2 emissions between farm gate and seaport for selected regions in Brazil. To estimate the impact of logistics on competitiveness of soybeans in a national comparison, the export processes
over four different distances (2,200 km; 1,400 km; 1,050 km; 500 km) to the principal seaport for
soybean exports, i.e., Santos, were evaluated in a market and a cost analysis.
Few large trading companies dominate 80 % of the national soybean trade. Cooperatives, who
offer their services to farmers, cover 20 % and are mainly active in the southern regions. In the
Center West, in contrast, producers and farmer unions typically negotiate directly with the trading companies.
The transport sector is fragmented. Road transport service providers act on a highly competitive
market while only one railway service provider operates rail transports on the ALLMN railroad
from Mato Grosso to Santos. Primarily, the large trading companies have access to rail transportation and export up to 80 % of their trading soybean volume originated in the Center West on
the ALLMN railroad. Smaller companies ship their lower trading volumes mostly with trucks, due
to high rail freight prices.
The state Mato Grosso is the principal soybean producer of the country and situated in central
Brazil far from the seaports. As soybeans are a high volume and low value-added bulk good, the
most efficient transportation modalities over long distances are in economic and climatic terms
the rail and water mode. However, the infrastructural development in Brazil did not keep pace
with the increase in agricultural production. Insufficient capacities of storage, rail and waterway
transportation create inefficiencies and prejudice the competitiveness of the Brazilian soybeans
produced in remote areas.
Road transport by truck accounts for 60 % of soybean transport in Brazil. Due to high diesel consumption and the fact that inland production regions are distant from ports, markets costs for
soybeans produced in the remote areas of the Center West are generally high relative to other
production regions closer to the seaports. The cost analysis for soybean export processes over
four different distances (2,200 km; 1,400 km; 1,050 km; 500 km) revealed total logistics costs of
92.61 R$/t for transports from Barretos to the Santos (April 2011), which is closest to Santos port
(500 km), and 230.08 R$/t for transports from Sorriso, which is the most distant but major soybean production region (2,200 km). Costs per tonne-kilometer varied from 0.19 R$/tkm for
500 km transports (Barretos to Santos) and 0.15 R$/tkm for 1,050 km (Rio Verde-Santos) to
0.13 R$/tkm for 1,400 km (Rondonópolis-Santos) and 0.10R$/tkm for transports over 2,200 km
(Sorriso-Santos).
88
Chapter 7
Summary
Logistics costs are composed by freight costs and export expenses, which include taxes, commission and brokerage, port fee and costs for damage of cargo. The calculation with data from April
2011 revealed that freight costs determine the major cost position of logistics costs, representing
66 % (September 2011: 63 %) in the case of Barretos and 86 % (September 2011: 84 %) in the
case of Sorriso. For inland transports over 1,050 km from Rio Verde the transport costs represented 79 % and for 1,400 km from Rondonópolis 82 %, respectively.
Rail freight rates are on a comparable level with road freight prices or may be even more expensive. Due to lacking data, rail transport costs were not analyzed in detail.
Because transport costs represent the major element of total logistics costs, the costs of truck
transportation were assessed. The major cost positions, which account for 63 % to 70 % of the
total transport costs, are fuel cost, driver's gross labor rate and maintenance. The calculation was
based on data from March 2011. Disparities of 71 % in total transportation costs were observed
when comparing the transport from Barretos (4.18 R$/km; 0.11 R$/tkm) to the transport from
Sorriso (3.42 R$/km 0.09 R$/tkm). Because fuel costs are related to the travelled distance, this
cost position gained weight with increased distances. For the shortest distance, toll costs constituted a major element of 29 % of the transport costs, as highways are mainly tolled in the region
close to Santos port. For the other distances this share varied from eight to sixteen percent.
The study showed that costs of truck transport and freight prices may differ as freight rates are
demand driven. They increase, e.g., during harvest time when transport services are highly demanded and capacities get scarce. It was revealed that the more distant the production region,
the higher the impact of transport costs on the local producer price. As higher logistics costs diminish the price received by the producer, elevated transport costs compromise the competitiveness of the production regions in the Center West. The sensitivity analysis demonstrated that
a change in a factor is magnified by the distance. Any factor variation that results in higher
transport costs would prejudice the producers in the Center West by a higher percentage than
the producers of the production regions closer to the export port. Higher transport costs would
reduce the producer price in the distant regions even more as logistics costs increase.
The assessment of GHG emissions that result from the soybean inland transport revealed that
road transport is costly due to the high fuel consumption and corresponding CO2 emissions. The
documentation of the CO2 emissions in a greenhouse gas inventory elucidated that the expansion
of agricultural production to remote areas negatively affects climate change due to the longer
the transport distances to the seaports. The observed CO2 fluxes road transports increased proportionally to the distance from 18.92 kgCO2eq/t (500 km) to 83.24 kgCO2eq/t (2,200 km).
The analysis showed that intermodal transports are more competitive than unimodal transports.
A GHG reduction potential of nine percent for transports starting in Rondonópolis and of 17 % for
transports from Sorriso was revealed. A point of weakness in intermodal transportation is the low
loading factor of the train at return. An increase of the loading factor offers potentials of up to
Chapter 7
Summary
89
24 % (13.72 kgCO2eq/t) to decrease GWP of soybean exports on the ALLMN railroad. In the case of
unimodal road transports, the highest potential of climate change mitigation bears the improvement of fuel efficiency of trucks.
The large and steadily increasing Brazilian soybean production in remote areas calls for more
public investments in infrastructure and for an expansion of the infrastructural network. Private
companies invest particularly in port capacities to mitigate inefficiencies arising from insufficient
infrastructure. The government implements measures for improving infrastructure to avoid further increases in transport costs and reduce inefficiencies arising from lacking infrastructure.
Strategic policies, like the rail law, give incentives for a higher utilization of the existing infrastructural capacities, which may positively influence rail freight rates. On the other hand, legislation
regulating the road transport sector like the truck drivers law reportedly drives road freight prices. The target to renew the Brazilian truck fleet, 32 % of which are older than 20 years, might be
a further price driver in the transport sector. Long and complicated bureaucratic processes hamper the realization of projects in planned time spans. However, recent initiatives to improve the
national infrastructure could lead to large improvements in the soybean logistics.
An efficient integrative logistics system, which is supported by a set of strategic investments and
policies, is essential to keep costs and GHG emissions of the export processes on national level at
a minimum and ensure mass flows without breakdowns. The strategic integration of any agent,
institution and process is crucial to ensure the competitiveness of the Brazilian soybean export
because at last a "trade supply chain is only as strong as its weakest link" (WORLD BANK, 2010a).
Chapter 8
8
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World Bank (2012): Data By Country [November 19, 2012]. Online accessed on November
19, 2012, at: http://data.worldbank.org/country/brazil?display=graph
World Trade Press (2007)
World Trade Press (2007): Precipitation Brazil figure, Temperature Brazil figure. Petaluma,
California/USA.
Yin (2009)
Yin, Robert K. (2009): Case study research: design and methods. Applied Social Research
Methods Series, Volume 5, 4th edition, SAGE: Los Angeles/USA [a.o.].
Chapter 8
References
107
Personal contact
ALL (n.d.)
América Latina Logística [ALL] (n.d.): TAG - Alto Araguaia (MT). Information material
provided by ALL via Email on November 8, 2012.
ALL (n.d.a)
América Latina Logística [ALL] (n.d.): ALL – América Latina Logística. Information material
provided by ALL via Email on November 8, 2012.
Alves (2012)
Alves, Prof. Dr. Lucilio Rogerio A., Professor, ESALQ/USP and ESALQ CEPEA. Expert interview on August 16, 2012, Piracicaba-SP/Brazil.
Amaral (2012)
Amaral, Daniel Furlan, Economics manager, Abiove. Expert interview on August 20, 2012,
São Paulo-SP/Brazil.
Anonymous 1
Anonymous 1, Logistics Manager Brazil, Trading company (volume of soybeans traded
≥ 2 mmt/year). Expert interview on September 14, 2012, São Paulo-SP/Brazil.
Anonymous 2
Anonymous 2, Logistics supervisor, Trading company (volume of soybeans traded
≥ 2 mmt/year). Expert interview on September 19, 2012, Santos-SP/Brazil.
Anonymous 3
Anonymous 3, Commercial manager - Grain origination, Trading company (volume of soybeans traded ≥ 2 mmt/year). Expert interview on August 29, 2012, RondonópolisMT/Brazil.
Anonymous 4
Anonymous 4, Logistics manager, Trading company (volume of soybeans traded
≥ 2 mmt/year). Expert interview on August 29, 2012, Rondonópolis-MT/Brazil.
Anonymous 5
Anonymous 5 Manager - soybean complex, soy logistics for SP, GO, MG; Manager - soybean complex, soy logistics Center West of Goiás, Trading company (volume of soybeans
traded ≥ 2 mmt/year). Expert interview on September 04, 2012, Rio Verde-GO/Brazil.
Anonymous 6
Anonymous 6 (2012), Analista administrativo - grains, Trading company (volume of soybeans traded ≤ 1 mmt/year). Expert interview on August 24, 2012, Cuiabá-MT/Brazil.
108
Chapter 8
References
Antunes (2012)
Antunes, Elisandro, Commercial manager MT/GO, Agotran (Agostinetto Transportes de
Cereais Ltda.). Expert interview on August 28, 2012, Rondonópolis-MT/Brazil.
Bedoya (2012)
Bedoya, Daniel Marcelo Velazco, Research analyst, USP CEPEA. Email and Skype contact
between July 2012 and December 2012.
Birkhan (2012)
Birkhan, João Augusto, Export manager, CENTROGRÃOS. Expert interview on August 23,
2012, Cuiabá-MT/Brazil.
Dias (2012)
Dias, Paulo Cezar, Farm director, Fazenda California. Expert interview on September 05,
2012, Rio Verde-GO/Brazil.
Drescher (2012)
Drescher, Johnny, Commercial manager, MEGA Tierernährung. Personal information,
August 01, 2012.
Ferreira (2012)
Ferreira, Daniel Latorraca, Manager, IMEA (Instituto Mato-Grossense de Economia
Agropecuária). Expert interview on August 23, 2012, Cuiabá-MT/Brazil.
França (2012)
França, Fábio, Commercial manager - soybean complex, Trading company (volume of soybeans traded ≥ 2 mmt/year). Expert interview on August 30, 2012, Alto AraguaiaMT/Brazil.
Freitas (2012)
Freitas, Israel, COMIGO (Cooperativa Agroindustrial dos Produtores Rurais do Sudeste
Goiano). Email on December 05 and 06, 2012.
Garcia (2012)
Garcia, Carlos Henrique, Logistics manager, Ki-Barato Serviços e Com. Produtos e Cereais.
Expert interview on August 30, 2012, Alto Araguaia-MT/Brazil.
Garcia da Silva, 2012
Garcia da Silva, Cicero, Commercial manager, Brasil Central. Expert interview on September 03, 2012, Rio Verde-GO/Brazil.
Godinho (2012)
Godinho, Edson, Commodity trader. Email, August 27, 2012.
Chapter 8
References
109
Hüner, 2012
Hüner, Ulrich, Purchasing manager, HaBeMa Qualitätsfutter. Personal information, August 01, 2012, Hamburg/Germany.
Jesus (2012)
Jesus, Rafael, Commercial supervisor Cuiaba - MT, Bom Jesus transportes. Expert
interview on August 24, 2012, Cuiabá-MT/Brazil.
Junqueira (2012)
Junqueira, Paulo Carneiro, Production supervisor, COMIGO (Cooperativa Agroindustrial
dos Produtores Rurais do Sudeste Goiano). Expert interview on September 11, 2012, Rio
Verde-GO/Brazil.
Klasener (2012)
Klasener, Solano, Farm manager, Sementes Arco Iris. Expert interview on August 30, 2012,
Alto Araguaia-MT/Brazil.
Niedermeier (2012)
Niedermeier, Valdir Antônio, Director and owner, Sementes Araguaia - Fazenda Janice.
Expert interview on August 31, 2012, Alto Araguaia-MT/Brazil.
Nunes (2012)
Nunes, Priscilla Biancarelli, Research analyst, Research group on agribusiness logistics,
USP Esalq-Log. Expert interview on August 15, 2012, Piracicaba-SP/Brazil.
Osaki (2012)
Osaki, Mauro, Research analyst, USP CEPEA. Expert interview on August 16, 2012, Piracicaba-SP/Brazil.
Parreira (2012)
Parreira França, Diogo, Fleet director, Brasil Central. Expert interview on September 04,
2012, Rio Verde-GO/Brazil.
Peeters (2012)
Peeters, Charles L., Farm director, APP GO Agropecuária. Expert interview on September
04, 2012, Rio Verde-GO/Brazil.
Rogério (2012)
Ferreira, Rogério (2012), Logistics manager, ROMA Transportadora. Expert interview on
September 12, 2012, Ribeirão Preto-SP/Brazil.
Silva M.(2012),
Silva, Moazir, Transport administrator, COMIGO (Cooperativa Agroindustrial dos
Produtores Rurais do Sudeste Goiano). Expert interview on September 11, 2012, Rio
Verde-GO/Brazil.
110
Chapter 8
References
Sperandio et al. (2012)
Sperandio, Luis Carlos, - President, Rural syndicate Alto Taquari-MT,
Emerson, Alexandre Tieppo, Municipal secretary, Municipal secretary of environment and
agriculture Alto Taquari-MT,
Eroci, Antonio Scaini, President, ACEATA
Dahlem Junior, Jaime Luiz, Coordinator, Municipal secretary of environment and agriculture Alto Taquari-MT. Expert interview on August 31, 2012, Alto Taquari-MT/Brazil.
Strauss (2012)
Strauss, Patric, Commodity broker, Amme & Müller. Personal contact, July 31, 2012, Hamburg/Germany.
Appendices
Appendices
Appendix 1:
A3
Exemplary calculations of soybean production costs – international
comparison
a) Brazil-USA. Average 2010 costs of production at various R$/$ exchange rates
Brazil
United
States
Heartland
Paraná
(Actual)
Mato Grosso
(Actual)
R$1.76/$1
Paraná
Mato Grosso
Paraná
(Hypothetical)
R$2.00/U$1=2.0
Mato Grosso
(Hypothetical)
R$1.50/$1
Seeds
Fertilizer
Chemical inputs
Labor
Other operational costs
ct/kg
ct/kg
ct/kg
ct/kg
ct/kg
4.14
1.22
1.20
1.23
3.25
2.19
2.00
1.74
0.56
1.86
1.01
4.68
3.17
0.77
1.60
1.93
1.76
1.54
0.49
1.64
0.89
4.12
2.79
0.68
1.41
2.57
2.35
2.05
0.65
2.19
1.19
5.49
3.72
0.90
1.88
Total variable costs
ct/kg
11.04
8.35
11.23
7.35
9.89
9.80
13.18
Land
Physical capital
Other fixed costs
ct/kg
ct/kg
ct/kg
12.58
5.37
0.67
3.09
5.33
1.30
1.88
2.45
1.47
2.72
4.69
1.15
1.65
2.16
1.29
3.63
6.25
1.53
2.20
2.87
1.72
Total fixed costs
ct/kg
18.62
9.72
5.79
8.56
5.10
11.41
6.79
Total
ct/kg
29.66
18.07
17.02
15.91
14.98
21.21
19.97
Source: CONAB, "Custo de Producao: Soja Plantio Directo" (May 2010); USDA, "Soybean Production and Returns" (2010); IMF, Exchange
rates. Cited in USITC (2012, p6-10).
b) USA-Argentina-Brazil. Soybean production costs
USA
Argentina
Brazil
South (RS)
Center West (MT)
Northeast (BA)
Seeds
Fertilizer
Pesticides
Operations
Labor
Other variable costs
Total variable costs
Depreciation
Land
Other fixed costs
Total fixed costs
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
US$/ha
144
58
39
51
60
2
354
218
299
62
579
46
39
60
96
16
137
394
144
244
0
388
48
115
81
90
30
80
444
87
174
34
295
55
240
97
66
39
138
635
55
96
35
186
34
196
92
85
17
81
505
37
58
23
118
Total
US$/ha
933
782
739
821
623
Source: Agroconsult and Conab (2010/11) cited in Aprosoja (2012). Modified by author.
A4
Appendices
Appendix 2:
Soybean price – domestic price and CBOT quotation (US$/t, 1995-2005)
US$/t
200
CBOT quotation
Domestic price
150
100
50
Jan
95
Jan
96
Jan
97
Jan
98
Jan
99
Jan
00
Source: MAPA (2007, p46).
Appendix 3:
Brazil – macroregions and states
P
Source: USITC (2012, p. 1-7).
Jan
01
Jan
02
Jan
03
Jan
04
Jan
05
Source: WORLD TRADE PRESS (2007).
Source: WORLD TRADE PRESS (2007).
a) Precipitation Brazil.
Climate charts – Brazil
Appendix 1. Climate charts Brazil.
Appendix 4:
b) Temperature Brazil.
Appendices
A5
A6
Appendices
Appendix 5 und 6:
mmt
Soybean exports and imports by country (2008-2013)
mmt
Soybean exports
120
Soybean imports
100
100
80
80
60
60
40
40
20
20
0
0
2008/09
Brazil
2009/10
USA
2010/11
Argentina
2011/12 2012/13
(est.)
Paraguay
Canada
2012/13
(est.)
2008/09
Other
2009/10
China
2010/11
EU-27
Mexico
2011/12 2012/13
(est.)
Japan
Taiwan
2012/13
(est.)
Other
Source: Created by author. Based on data from USDA PSD (2012, table 7).
Appendix 7:
World soybean consumption and exports (1960-2012)
mmt
300
250
World soybean consumption
World soybean exports
Chinese soybean consumption
Chinese soybean imports
200
150
100
50
0
1960/
1961
1965/
1966
1970/
1971
1975/
1976
Source: USDA PSD (2012, custom query).
1980/
1981
1985/
1986
1990/
1991
1995/
1996
2000/
2001
2005/
2006
2010/
2011
Appendices
Appendix 8:
A7
Cerrado region
Source: USITC (2012, p2-2).
A8
Appendix 9:
Appendices
Brazilian agricultural policy development (1965-2005)
Source: USITC (2012, p3-24).
Appendices
Appendix 10:
A9
Projection of world soybean production
Annotations by author:
Produção [portug.] = Production [engl.]
Área Brasil [portug.] = Area Brazil [engl.]
Source: HIGHQUEST PARTNERS cited by APROSOJA (2010).
Appendix 11:
GDP and soy production of selected regions (2009)
Gross domestic product (106 R$)
Total
Sorriso
Rondonópolis
Rio Verde
Barretos
Mato Grosso
Goiás
São Paulo
Brasil
Agriculture
Soy production
Industries
Service sector
Quantity (t)
Area (ha)
2,043
3,334
3,883,063
1,534
647
192
676
96
223
797
1,407
295
1,173
2,345
1,780
1,143
1,840,800
223,200
735,000
17,640
590,000
72,000
245,000
7,000
33,393
44,753
611,970
10,744
5,978
11,265
6,230
11,624
193,981
16,419
27,151
406,724
17,962,819
6,809,187
1,327,105
5,831,468
2,315,888
494,551
1,842,253
105,163
539,316
1,197,774
57,345,382
21,761,782
Source: IBGE (2010a), IBGE (2012).
A10
Appendices
Appendix 12:
Brazilian soybean exports by port (2009-2011)
MANAUS - AM
2009
SALVADOR - BA
2010
TUBARÃO - ES
2011
SAO LUIS - MA
SAO FRANCISCO DO SUL - SC
RIO GRANDE - RS
PARANAGUA - PR
SANTOS - SP
0%
5%
10%
15%
20%
25%
30%
35%
Appendix 13:
Soybean exports via Santos port (2010-2011)
mmt
2.5
Total Exports 2011
Exports to China 2011
Total Exports 2010
Exports to China 2010
2.0
1.5
1.0
0.5
0.0
Jan
Feb
Mar
Apr
May
Jun
Source: Created by author. Based on data from MDIC (2012).
Jul
Aug
Sep
Oct
Nov
Dec
Appendices
Appendix 14:
A11
Privatization efforts of the Brazilian government in the 1990s
Source: USITC (2012, p3-15)
Appendix 15:
Transport costs related to modality, distance and relative fuel efficiency
Total cost
Us$/km
Truck
Rail
Barge
Truck
(155)
Source: USDA (2010, p500).
Rail
(413)
Barge
(576) => Ton-Miles/Gallon
Distance
A12
Appendix 16:
Appendices
Mean values of freights for soybeans
(620-930 miles, February 2001 to March 2004.)
US$/metric ton
30
25
28.41
20
23.19
15
14.22
10
5
0
Road
Source: SIFRECA cited by CAIXETA-FILHO (2006, p2
Railway
Waterway
Appendices
A13
Appendix 17:
State of the national road network by state (2011)
Brasil
9.0
Norte
18.8
Rondônia
38.0
Amazonas
22.5
Roraima
Pará
Amapá
Tocantins
33.4
38.2
7.8
Acre
20.3
47.5
27.4
34.5
7.8
35.2
9.6
-34,1
25.3
15.5
Maranhao
26.6
21.0
7.0
Ceará
Rio Grande do Norte
6.0
Pernambuco
10.7
Alagoas
20.4
7.6
22.9
3.2
25.0
16.8
2.1
27.8
5.7
Mato Grosso do Sul
Goiás
Districto Federal
Source: CNT (2012a, pp313, 318).
12.4
6.7
13.0
25.6
9.6
14.0
15.7
45.2
19.7
11.6
49.9
38.5
30.1
18.0
10.0
47.7
27.3
32.4
3.0
15.9
29.3
38.4
13.2
29.8
31.8
38.6
12.8
28.8
38.2
31.5
1.8
10.8
28.1
7.2
21.9
6.9
20.6
40.0
29.3
18.7
2.4
20.9
5.1
4.5
30.3
16.5
1.5
29.0
24.4
11.8
4.0
4.9
27.8
3.6
Centro-Oeste
34.1
25.4
45.1
Paraná
1.8
47.9
Sao Paulo
Mato Grosso
27.5
36.6
Rio de Janeiro
Santa Catarina
Rio Grande do Sul
32.4 7.1
39.4
22.9
Sul
9.0
45.2
5.6
Minas Gerais
Espírito Santo
30.8
27.1
9.9
Sudeste
37.9
36.8
13.8
Bahia
4.1
4.9
28.7
23.7
2.9
Sergipe
0.4
24.7
15.3
23.2
3.9
29.0
38.2
9.2
Paraiba
15.4
41.7
23.9
1.9
26.4
41.4
18.3
8.3
8.8
38.1
23.4
Very good
0.8
19.2
41.5
19.4
Piauí
10.6
42.3
10.6
Good
6.4
34.7
32.1
Nordeste
Bad
Regular
22.9
36.9
11.9
Poor
30.4
45.4
17.0
0.7
0.9
-27,1
47.1
9.9
33.3
7.8
1.8
50.0
11.0
A14
Appendix 18:
Appendices
Brazilian cargo railways
Source: ANTF (2012a)
Appendix 19:
Extension of the rail network under concession by operator
Operators regulated by ANTT
Railroad network - extension in km
gauge
América Latina Logística Malha Oeste S.A. - ALLMO
Ferrovia Centro-Atlântica S.A. - FCA
MRS Logística S.A. - MRS
Ferrovia Tereza Cristina S.A. - FTC
América Latina Logística Malha Sul S.A. - ALLMS
Estrada de Ferro Paraná Oeste S.A. - FERROESTE
Estrada de Ferro Vitória a Minas - EFVM
Estrada de Ferro Carajás - EFC
Transnordestina Logística S.A. - TLSA
América Latina Logística Malha Paulista S.A. - ALLMP
América Latina Logística Malha Norte S.A. - ALLMN
VALEC/Subconcession: Ferrovia Norte-Sul - FNS
Total
Source: ANTT (2012a, p4). Modified by author.
Total
1.60 m
1.0 m
Mixed
1.63
892.00
1.46
617.00
720.00
1.95
7.91
164.00
7.25
248.00
905.00
4.19
243.00
-
156.00
42.00
11.00
18.00
283.00
720.00
1.95
8.07
1.67
164.00
7.27
248.00
905.00
892.00
4.21
1.99
617.00
-
5.32
22.86
510.00
28.69
Appendices
A15
Appendix
20:
Inland waterways and navigable rivers
Appendix 1. Inland waterways and navigable rivers.
Source: PERRUPATO (2011). Modified by author.
Annotation of author: The extension of the navigable rivers is represented by the dark blue lines. The red arrows point
at the waterways of relevance for the selected regions (see chapter 2.2), i.e. the Madeira, Tapajós and Paraná-Tietê
waterways. The red circle encompasses the major soybean production region of central Mato Grosso.
Source: PERRUPATO (2011). Modified by author.
A16
Appendices
Appendix 21:
Soybeans - conditions at storage
Source: ACASIO (n.d., pp6, 7)
Table 1. Safe storage periods for soybeans at some moisture levels
Moisture content,
Safe storage period
% wet basis
Market stock
Seed Stock
10-11
10-12.5
13-14
14-15
4 years
1 - 3 years
6 - 9 months
6 months
1 year
6 months
poor germination
poor germination
Source: Barre (1976).
Appendix 22:
Export
standard
values
Brazilian standard values for soybean exports
Impurities
Humidity
Breakage
Rotten
Damaged
≤1
≤ 14
≤ 30
≤4
≤8
Source: Created by author. Based on data from ANONYMOUS 1 (2012).
%
%
%
%
%
Appendices
A17
Appendix 23:
Brasil
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
Total volume of agricultural bulk goods produced (t) in 2011 vs. storage
capacities (status quo 12/2012)
5,070,717
13,476,994
3,435,366
55,660,415
74,815,447
1,931,135
5,690,043
Center
West
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
3,187,523
1,013,618
588,836
17,399,944
33,768,154
1,211,803
102,329
East
South
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
154,18
193,914
817,031
9,998,944
4,212,294
452,072
168,653
Total
160,080,117
Total
57,272,207
Total
15,842,908
Storage capacities
Difference
118,222,783
-41,857,334
Storage capacities
Difference
44,663,410
-12,608,797
Storage capacities
Difference
13,379,860
-2,463,048
South
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
3,193
10,112,953
1,091,132
21,896,967
28,666,010
44,992
5,419,061
North
East
Total
67,234,308
Storage capacities
Difference
51,267,099
-15,967,209
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
1,707,168
1,165,618
818,484
5,039,170
6,228,019
206,202
North
Total
15,164,661
Total
4,411,853
Storage capacities
Difference
6,802,380
-8,362,281
Storage capacities
Difference
2,110,034
-2,301,819
Annotation of author: No historical data for data on storage capacities available. Data refer to December 2012.
Source: Created by author. Data from SIDRA IBGE (2012) and CONAB (2012).
Appendix 24
Distribution of warehousing capacities by location
Port
On-Farm
6%
14 %
36 %
44 %
Urban
Source: MORCELI (2012). Modified by author
Rural
Cotton
Rice
Beans
Corn
Soybeans
Sorghum
Wheat
18,653
990,891
119,883
1,325,390
1,940,970
16,066
A18
Appendices
Appendix 25:
Development of investments in infrastructure of federal government (19752011) (Investments/GDP
%) infrastructure of federal government (1975-2011)
Appendix 1. Development
of investmentsin in
(Investments/GDP in %).
Source: CNT (2012, p328).
Source: CNT (2012, p328).
Appendix 26:
2008-2023
Road
Rail
Water
Port
Air
Total
PNLT investments (2008-2023)
Extension (km)/
Quantity
Investments
(billion R$)
Share of total
investments (%)
43.20
20.26
14.49
169.00
40.00
74.19
50.56
12.81
25.16
9.70
43
29
7
15
6
172.41
100
Source: Created by author. Based on Ministry of Transport/Ministry of Defense (2007).
Appendices
A19
Appendix 27:
Distribution of global GHG emissions by sector
Waste amd wastewater
3%
Forestry
Energy supply
17 %
Agriculture
26 %
14 %
13 %
19 %
Transport
8%
Industry
Residential &
Commeercial buildings
Source: EPA (2012).
Appendix 28:
Phases of the Airquality Program for Automotive Vehicles
Emmissions reductions during the phases of the Air Quality
Program for Automotive Vehicles (Proconve, in Portuguese)
Emissions (%)
100
87
81
86
95
90
80
70
60
50
40
30
20
10
0
CO
P2 (Euro 0)
HC
P3 (Euro 1)
NOX
P4 (Euro 2)
P5 (Euro 3)
MP
P6 (Euro 4)
P7 (Euro 5)
Annotation of author: Permitted limits for exhaust emissions of new vehicles sold in EU member states, defined in
the European emission standards. For detailed information see e.g.
http://ec.europa.eu/enterprise/sectors/automotive/environment/eurovi/index_en.htm
(accessed on November 29, 2012).
Source: IPEA (2011, p126).
A20
Appendices
Appendix 29:
Emission factors considered
CO2 (kg/l)
IPCC
GEMIS
Brazil 2002
cited by
Bartholomeu 2003
Bartholomeu 2001
cited by
Bartholomeu 2006
Bartholomeu
2006
2.67
2.68
2.80
2.70
2.75
Source: BARTHOLOMEU (2006, pp61).
Appendix 30:
Frequently travelled warehouse-port routes
Route
Highways
Ia
Sorriso/MT - Cuiabá/MT - Jataí/GO - São José do Rio
Preto/SP - Araraquara/SP - Santos/SP
BR-1633 - BR-3643 - BR-1533 - SP-310 - SP-3301 SP-3481 - SP-1601
Ib
Sorriso/MT - Cuiabá/MT - Campo Grande/MS Araçatuba/SP - Campinas/SP - Santos/SP
BR-1633 - BR-3643 - BR-1633 - BR-2623 - SP-300 SP-2801 - SP-1601
II
Rondonópolis/MT - Jataí/GO - São José do Rio Preto/
SP - Araraquara/SP - Santos/SP
BR-3643 - BR-1533 - SP-310 - SP-3301 - SP-3481 SP-1601
III
Rio Verde/GO - Itumbiara/GO - Uberaba/MG Ribeirão Preto/SP - Campinas/SP - Santos/SP
BR-452 - BR-153 - BR-2623 - SP-3301 - SP-3481 SP-1601
IV
Barretos/SP - Araraquara/SP - Santos/SP
SP-3262 - SP-310 - SP-3481 - SP-1601
Road classification: 1 very good - 2 good - 3 regular
Source: Created by author. Based on expert discussions.
Appendices
Appendix 31:
A21
Tower-silo storage on farm in Alto Araguaia-MT
Appendix 1. Tower-silo storage on farm in Alto Araguaia-MT.
Capacity: 11,400 t.
Capacity: 11,400 t.
Source: Picture taken by author. Information from KLASENER (2012).
Source: Picture taken by author. Information from KLASENER
Appendix 32:
Tolled highway tracks on exemplary route
Source: Mapeia (2012) using Google Maps (10/12/2012).
Exemplary roads (MT, GO) Source: Photos taken by author.
MT-100 (municpality Alto Araguaia-MT).
Appendix 33
Unpaved municipal road in dry-season (county Rio Verde--GO).
BR-060 (county Rio Verde-GO) - roadworks.
A22
Appendice
Appendices
A23
Appendix 34:
Volume transported on the ALLMN railroad (mmt)
mmt
1.2
1.0
0.8
0.6
0.4
2007
0.2
2008
2009
2010
0.0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annotation of author: As soybeans and corn are the major transported products on the ALLMN railroad (see chapter
2.3.2), this figure is representative for the seasonal demand for transportation services for grain shipping.
Source: Created by author. Based on ANTT (2010)/ANTT (2009)/ANTT (2008).
A24
Appendix 35:
Appendices
Warehousing fees (2010)
Source: SIARMA (2010).
Appendices
A25
Appendix 36:
Soybean world market prices - time series 1995-2012
R$
1 400
1995-2012
1 200
1 000
800
600
400
200
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
Jan
May
Sep
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
R$
1 400
2008
2009 2010
2011 2012
Oct 2007 - Oct 2012
1 200
1 000
800
600
400
200
2007
2008
Source: INDEXMUNDI (2012)
2009
2010
2011
2012
Oct
Aug
Jun
Apr
Feb
Dec
Oct
Jun
Aug
Apr
Feb
Dec
Oct
Jun
Aug
Apr
Feb
Oct
Dec
Jun
Aug
Apr
Feb
Oct
Dec
Aug
Jun
Apr
Feb
Dec
Oct
0
A26
Appendices
Appendix 37:
Local spot market prices (2007-2012)
R$/Sack
80
Mogiana
Rio Verde
Rondonópolis
Sorriso
70
60
50
40
30
2007
2008
2009
2010
2011
Oct
Jul
Apr
Jan
Oct
Jul
Apr
Jan
Oct
Jul
Apr
Jan
Oct
Jul
Apr
Jan
Oct
Jul
Apr
Jan
Oct
Jul
Apr
Jan
20
2012
Source: Created by author. Based on data from CEPEA.
Appendix 38:
Calculation of total logistics costs of soybean exports for selected regions
(September 2011)
SEPTEMBER 2011
Origin
Distance to Santos
Sorriso-MT
2,200 km
Rondonópolis-MT
1,400 km
Rio Verde-GO
1,050 km
Barretos-SP
500 km
185.02
136.92
106.16
60.75
1. FOB Santos
2. Exchange rate
US$/t
R$/US$
504.85
1.75
4. Export expenses
4.1 Port fee
4.3 Taxes
4.4 Damage
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
883.39
35.04
17.50
13.12
0.00
4.42
6. Freight rate
R$/t
R$/t
848.35
R$/t
R$/t
R$/t
220.06
663.33
689.50
171.96
711.43
746.17
141.19
742.19
755.83
95.79
787.60
790.83
Difference [9]-[8]
R$/t
%
%
26.17
3.95
84
34.74
4.88
80
13.64
1.84
75
3.24
0.41
63
Share [6]/[7]
Appendices
A27
Appendix 39:
Local fuel prices of selected regions (2004-2011)
R$/l
2,4
+11%
2,2
+8%
2,0
+13%
1,8
1,6
1,4
1,2
2004
2005
2006
2007
Sorriso
Rondonópolis
2008
Rio Verde
2009
2010
2011
Barretos
Source: Created by author. Based on data from ANP provided by BEDOYA (2012).
Appendix 40:
Fuel efficiency versus fleet age
Fuel efficiency (L/100 km)
50
45
Medium trucks
Heavy trucks
40
35
30
25
20
15
10
5
0
2 years
and less
Total
3-5 years
Source: TRANSPORT CANADA (2005) cited by IEA (2009).
6-9 years
10-13 years
14 years
and more
A28
Appendix 41:
Appendices
GHG intensity of selected maritime freight transport modes
Ro-Ro
Container vessel
(2 000-8 000 + TEU)
Container vessel
(coasted <2 000 TEU)
Bulk carrier
(10 k - 200 k dwt)
Products tanker
(5 k - 60 k dwt)
Crude oil carrier
(10 k - 200 k dwt)
1
2
3
4
5
10
20
30
40 50
GHG intensity (g CO2eq /tkm, log scale)
Source: ITF ESTIMATES and BUHAUG ET AL. (2008) cited by IEA (2009, p347)
Appendix 42: Development of
Brazilian road network
Source: Aprooja (2012).
the Brazilian waterway network
Brazilian railroad network
100
2011
National export destinations of soybeans
Source: Aprosoja (2011).
Appendix 45:
2023
Appendices
A29
A30
Appendices
Appendix 46
Exchange rate - Brazilian real (R$) / US dollar (US$)(2000-2012)
Exchange rate (IPEA)
Year.month
2000.01
2000.02
2000.03
2000.04
2000.05
2000.06
2000.07
2000.08
2000.09
2000.10
2000.11
2000.12
2001.01
2001.02
2001.03
2001.04
2001.05
2001.06
2001.07
2001.08
2001.09
2001.10
2001.11
2001.12
2002.01
2002.02
2002.03
2002.04
2002.05
2002.06
2002.07
2002.08
2002.09
2002.10
2002.11
2002.12
2003.01
2003.02
2003.03
2003.04
2003.05
2003.06
Year.month
1.8037
1.7753
1.7420
1.7682
1.8279
1.8083
1.7978
1.8092
1.8392
1.8796
1.9480
1.9633
1.9545
2.0019
2.0890
2.1925
2.2972
2.3758
2.4660
2.5106
2.6717
2.7402
2.5431
2.3627
2.3779
2.4196
2.3466
2.3204
2.4804
2.7140
2.9346
3.1101
3.3420
3.8059
3.5764
3.6259
3.4384
3.5908
3.4469
3.1187
2.9557
2.8832
Source: IPEA (2012).
2003.07
2003.08
2003.09
2003.10
2003.11
2003.12
2004.01
2004.02
2004.03
2004.10
2004.11
2004.12
2005.01
2005.02
2005.03
2005.04
2005.05
2005.06
2005.07
2005.08
2005.09
2005.10
2005.11
2005.12
2006.01
2006.02
2006.03
2006.04
2006.05
2006.06
2006.07
2006.08
2006.09
2006.10
2006.11
2006.12
2007.01
2007.02
2007.03
2007.04
2007.05
2007.06
Year.month
2.8798
3.0025
2.9228
2.8615
2.9138
2.9253
2.8518
2.9303
2.9055
2.8529
2.7860
2.7182
2.6930
2.5978
2.7047
2.5792
2.4528
2.4135
2.3735
2.3606
2.2944
2.2565
2.2108
2.2855
2.2739
2.1619
2.1520
2.1293
2.1781
2.2483
2.1893
2.1559
2.1687
2.1483
2.1579
2.1499
2.1385
2.0963
2.0887
2.0320
1.9816
1.9319
2007.07
2007.08
2007.09
2007.10
2007.11
2007.12
2008.01
2008.02
2008.03
2008.04
2008.05
2008.06
2008.07
2008.08
2008.09
2008.10
2008.11
2008.12
2004.04
2004.05
2004.06
2004.07
2004.08
2004.09
2009.01
2009.02
2009.03
2009.04
2009.05
2009.06
2009.07
2009.08
2009.09
2009.10
2009.11
2009.12
2010.01
2010.02
2010.03
2010.04
2010.05
2010.06
Year.month
1.8828
1.9660
1.8996
1.8010
1.7699
1.7860
1.7743
1.7277
1.7076
1.6889
1.6605
1.6189
1.5914
1.6123
1.7996
2.1729
2.2663
2.3944
2.9060
3.1004
3.1291
3.0368
3.0029
2.8911
2.3074
2.3127
2.3138
2.2059
2.0609
1.9576
1.9328
1.8452
1.8198
1.7384
1.7260
1.7507
1.7798
1.8402
1.7858
1.7576
1.8132
1.8059
2010.07
2010.08
2010.09
2010.10
2010.11
2010.12
2011.01
2011.02
2011.03
2011.04
2011.05
2011.06
2011.07
2011.08
2011.09
2011.10
2011.11
2011.12
2012.01
2012.02
2012.03
2012.04
2012.05
2012.06
2012.07
2012.08
2012.09
2012.10
2012.11
1.7696
1.7596
1.7187
1.6860
1.7133
1.6934
1.6748
1.6680
1.6591
1.5864
1.6135
1.5870
1.5639
1.5970
1.7498
1.7726
1.7905
1.8369
1.7897
1.7184
1.7953
1.8548
1.9860
2.0492
2.0287
2.0294
2.0281
2.0298
2.0678
Appendices
A31
Appendix 47:
Mês
Jan.2007
Feb.2007
Mrz.2007
Apr.2007
Mai.2007
Jun.2007
Jul.2007
Aug.2007
Sep.2007
Okt.2007
Nov.2007
Dez.2007
Jan.2008
Feb.2008
Mrz.2008
Apr.2008
Mai.2008
Jun.2008
Jul.2008
Aug.2008
Sep.2008
Okt.2008
Nov.2008
Dez.2008
Jan.2009
Feb.2009
Mrz.2009
Apr.2009
Mai.2009
Jun.2009
Jul.2009
Aug.2009
Sep.2009
Okt.2009
Nov.2009
Dez.2009
Jan.2010
Feb.2010
Mrz.2010
Apr.2010
Mai.2010
Jun.2010
Jul.2010
Aug.2010
Sep.2010
Okt.2010
Nov.2010
Dez.2010
Jan.2011
Feb.2011
Mrz.2011
Apr.2011
Mai.2011
Jun.2011
Jul.2011
Aug.2011
Sep.2011
Okt.2011
Nov.2011
Dez.2011
Jan.2012
Feb.2012
Mrz.2012
Apr.2012
Mai.2012
Jun.2012
Jul.2012
Aug.2012
Sep.2012
Okt.2012
Fob price and selected producer prices
Soja - Mogiana/SP Soja - Rio Verde/GO
Soja - Sorriso/MT
Disponível - sc/60 kg Disponível - sc/60 kg Disponível - sc/60 kg
30.16
32.10
31.05
28.33
28.35
29.44
30.37
32.66
36.92
39.22
40.86
44.04
44.91
47.75
45.21
42.87
42.84
47.91
49.67
43.05
45.66
43.98
44.40
44.26
47.22
47.65
44.26
46.16
48.82
49.32
47.22
47.11
46.21
44.54
45.85
45.89
39.94
36.03
34.89
34.59
35.01
36.17
37.88
40.28
42.17
45.13
48.43
49.53
50.86
50.54
45.26
43.83
43.72
44.73
43.97
44.97
47.45
45.21
44.27
43.66
43.88
46.26
50.61
55.94
58.58
60.41
71.16
79.45
79.63
67.41
29.14
29.66
29.94
26.77
27.14
29.28
30.45
32.72
37.55
39.69
40.88
42.43
43.38
44.94
42.47
41.11
41.31
45.78
47.08
40.56
42.27
40.66
41.04
40.96
43.94
43.06
40.71
42.75
45.24
45.55
43.85
44.34
42.90
42.52
42.21
41.54
37.30
31.96
30.38
30.95
32.02
32.47
34.99
38.68
40.81
42.76
44.86
47.01
45.95
45.01
41.50
40.18
40.62
41.14
41.50
42.88
45.35
43.09
41.97
40.69
43.13
41.90
46.75
52.32
57.07
61.36
71.11
76.02
76.42
70.51
Source: Data from CEPEA, provided by ALVES (2012).
23.65
23.87
23.14
22.50
22.99
23.82
25.21
27.92
31.55
32.91
35.38
36.34
37.04
38.16
36.18
34.54
36.53
41.61
42.12
35.96
38.16
36.62
37.17
36.36
38.91
36.67
34.97
38.02
40.80
40.95
39.21
39.96
38.77
37.92
37.80
36.20
28.89
25.73
24.73
26.21
27.91
29.14
31.57
35.17
36.95
39.36
42.80
42.98
40.64
39.50
37.66
35.95
36.53
37.43
37.46
38.98
41.37
39.61
38.19
37.24
37.77
36.71
41.91
48.43
54.04
57.89
66.00
70.10
71.54
65.96
Soja - Rondonópolis/MT
Disponível - sc/60 kg
26.23
26.69
26.09
24.78
25.86
27.03
28.57
31.77
35.57
36.76
39.13
39.54
40.98
42.13
40.03
38.97
40.14
45.18
45.56
39.58
41.12
40.43
41.05
39.89
42.21
39.49
38.62
41.57
44.34
45.06
43.07
43.85
42.78
42.09
41.58
38.83
31.66
29.15
28.76
29.45
31.13
32.50
35.11
38.58
40.16
42.78
46.78
46.51
43.44
42.51
41.19
39.31
40.19
40.68
41.25
42.50
44.77
43.29
41.35
40.31
40.84
40.21
45.65
52.48
57.58
61.32
70.93
75.38
77.32
70.56
Soja FOB Santos
382.53
362.01
359.95
371.58
366.83
373.60
406.32
426.57
454.91
481.00
497.41
513.89
535.57
535.09
513.69
515.95
511.71
521.40
549.24
545.58
504.85
469.13
461.19
457.10
479.50
498.35
531.78
560.37
551.30
545.82
nd
nd
608.44
573.34
A32
Appendices
Appendix 48:
ANO
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
Freight rates for selected routes (2011-2012)
MES
PRODUTO
ORIGEM
UF
Janeiro
Soja
Rio Verde GO
Fevereiro
Soja
Barretos
SP
Fevereiro
Soja
Rio Verde GO
Fevereiro
Soja
Sorriso
MT
Março
Soja
Barretos
SP
Março
Soja
Rio Verde GO
Abril
Soja
Barretos
SP
Abril
Soja
Rio Verde GO
Abril
Soja
Sorriso
MT
Maio
Soja
Barretos
SP
Maio
Soja
Rio Verde GO
Maio
Soja
Sorriso
MT
Junho
Soja
Barretos
SP
Junho
Soja
Rio Verde GO
Junho
Soja
Sorriso
MT
Julho
Soja
Barretos
SP
Julho
Soja
Rio Verde GO
Julho
Soja
Sorriso
MT
Agosto
Soja
Barretos
SP
Agosto
Soja
Rio Verde GO
Agosto
Soja
Rondonópolis MT
Setembro
Soja
Barretos
SP
Setembro
Soja
Rio Verde GO
Setembro
Soja
Rondonópolis MT
Outubro
Soja
Barretos
SP
Outubro
Soja
Rio Verde GO
Outubro
Soja
Rondonópolis MT
Outubro
Soja
Sorriso
MT
Novembro
Soja
Barretos
SP
Novembro
Soja
Rio Verde GO
Dezembro
Soja
Barretos
SP
Dezembro
Soja
Rio Verde GO
Janeiro
Soja
Barretos
SP
Janeiro
Soja
Rio Verde GO
Janeiro
Soja
Rondonópolis MT
Fevereiro
Soja
Barretos
SP
Fevereiro
Soja
Rio Verde GO
Fevereiro
Soja
Rondonópolis MT
Fevereiro
Soja
Sorriso
MT
Março
Soja
Rio Verde GO
Abril
Soja
Rio Verde GO
Maio
Soja
Barretos
SP
Maio
Soja
Rio Verde GO
Maio
Soja
Rondonópolis MT
Maio
Soja
Sorriso
MT
Junho
Soja
Rio Verde GO
Junho
Soja
Rondonópolis MT
DESTINO
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Santos
Source: Data from ESALQ-LOG, provided by NUNES (2012).
UF
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
R$/t
93.56
60.50
96.39
175.00
61.50
109.33
61.00
113.84
183.60
61.00
109.55
183.60
61.00
108.46
199.80
61.00
109.62
204.90
61.00
109.65
154.30
61.00
100.07
154.30
61.00
96.76
160.00
178.00
61.00
101.68
61.00
105.24
61.00
97.95
154.00
62.33
107.55
154.00
200.13
111.82
113.48
72.00
111.67
162.80
205.38
108.08
153.27
R$/t.km Distância (km)
0.0945
990
0.1205
502
0.0974
990
0.0791
2211
0.1225
502
0.1104
990
0.1215
502
0.1150
990
0.0830
2211
0.1215
502
0.1107
990
0.0830
2211
0.1215
502
0.1096
990
0.0904
2211
0.1215
502
0.1107
990
0.0927
2211
0.1215
502
0.1108
990
0.0990
1559
0.1215
502
0.1011
990
0.0990
1559
0.1215
502
0.0977
990
0.1026
1559
0.0805
2211
0.1215
502
0.1027
990
0.1215
502
0.1063
990
0.1215
502
0.0989
990
0.0988
1559
0.1242
502
0.1086
990
0.0988
1559
0.0905
2211
0.1129
990
0.1146
990
0.1434
502
0.1114
1002
0.1044
1559
0.0929
2211
0.1079
1002
0.0983
1559
Appendices
Appendix 49:
A33
Calculation of total logistics costs of soybean exports for selected (2012)
APRIL 2012
Origin
Distance to Santos
1. CBOT Chicago
2. Bonus Santos
3. FOB Santos
4. Exchange rate
6. Export expenses
6.1 Port fee
6.3 Taxes
6.4 Damage
8. Freight rate
11. Price quoted at local
spot market
Difference [10], [11]
Share of transport costs
over total logistics costs
Sorriso-MT
2,200 km
US$/Bushel*
US$/t
US$/bushel
US$/t
US$/bushel
US$/t
R$/sack
R$/US$
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/sack
R$/t
R$/sack
R$/t
R$/t
R$/sack
R$/sack
R$/t
R$/t
%
%
Rondonópolis-MT
1,400 km
Rio Verde-GO
1,050 km
Barretos-SP
500 km
14.34
527.07
0.91
33.44
15.25
560.37
62.36
1.85
1039.37
39.51
20.40
13.91
0.00
5.20
999.86
59.99
198.00
11.88
237.51
801.86
48.11
48.43
807.17
5.30
0.66
136.92
8.2152
176.43
862.94
51.78
52.48
874.67
11.72
1.36
n/d
n/d
52.32
872.00
-
n/d
n/d
55.94
932.33
-
83
78
n/d
n/d
A34
Appendices
S EPTEMBER 2012
Origin
Distance to Santos
1. CBOT Chicago
2. Bonus Santos
3. FOB Santos
FOB Santos
4. Exchange rate
6. Export expenses
6.1 Port fee
6.3 Taxes
6.4 Damage
Price at port gate
8. Freight rate
11. Price quoted at local
spot market
Difference ? [10], [11]
Share of transport costs
over total logistics costs
Sorriso-MT
2,200 km
US$/Bushel*
US$/t
US$/bushel
US$/t
US$/bushel
US$/t
R$/sack
R$/US$
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/t
R$/sack
R$/t
R$/sack
R$/t
R$/t
R$/sack
R$/sack
Rondonópolis-MT
1,400 km
Rio Verde-GO
1,050 km
Barretos-SP
500 km
16.84
618.77
-0.28
-10.31
16.56
608.44
36.51
2.03
1233.96
43.69
22.31
15.21
0.00
6.17
1190.28
71.42
245.74
14.7444
289.43
944.54
56.67
71.54
187.46
11.2476
231.15
1,002.82
60.17
77.32
n/d
n/d
76.42
n/d
n/d
79.63
R$/t
1,192.33
1,288.67
1,273.67
1,327.17
R$/t
%
247.80
26.23
285.85
28.50
-
-
85
81
n/d
n/d
%
Source: Author's calculation.
Appendices
Appendix 50:
A35
Calculation of transport costs for soybean shipping from selected regions to
Santos port
Fixed costs
average fixed capital
300,000 R$/year
Interest on capital
Depreciation
300,000 R$/year
7,500 R$/year
7,500 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
158,800 R$
1.99 R$/km
Variable costs
Total fixed costs per tkm
0.0536 R$/tkm
Mar 2011
Route 1
Origin
Sorriso, via MS Sorriso-MT, via GO Rondonópolis-MT Rio Verde-GO Barretos-SP
Route 2
Route 3
Route 4
Route 5
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
Fuel costs
R$/l
R$/route
2.33
2560.80
2.33
2444.40
2.24
2.08
1.97
1564.50
1092.53
493.25
R$/t
69.21
66.06
42.28
29.53
13.33
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3262.90
3022.60
2142.70
1697.33
1096.65
R$/tkm
0.0401
0.0389
0.0414
0.0437
0.0593
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7629.90
7191.10
4921.70
3781.58
2089.15
R$/tkm
0.09
0.09
0.10
0.10
0.11
R$/route/t
206.21
194.35
133.02
102.20
56.46
R$/t
213.33
213.33
163.33
109.33
61.00
7.12
18.98
30.31
7.13
4.54
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
A36
Appendices
Variable costs
Apr 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route/t
R$/t
Variable costs
Sep 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/t
R$/route
R$/t
R$/tkm
R$/route
R$/tkm
R$/t
R$/t
Route 1
Sorriso, via MS
2,200
2.31
2545.40
0.03
702.10
0.01
3247.50
0.0399
4367.00
0.05
7614.50
0.09
205.80
198.13
-7.67
Route 2
Sorriso-MT, via GO
2,100
2.31
2429.70
0.03
578.20
0.01
3007.90
0.0387
4168.50
0.05
7176.40
0.09
193.96
198.13
4.17
Route 3
Route 4
Route 5
Rondonópolis-MT Rio Verde-GO Barretos-SP
1,400
1,050
500
Route 1
Sorriso, via MS
2,200
2.34
2572.90
0.03
702.10
0.01
3275.00
0.0402
88.51
4367.00
118.03
0.05
7642.00
0.09
206.54
185.00
-21.54
Route 2
Sorriso-MT, via GO
2,100
2.34
2455.95
0.03
578.20
0.01
3034.15
0.0390
82.00
4168.50
112.66
0.05
7202.65
0.09
194.67
185.00
-9.67
Route 3
Route 4
Route 5
1,400
1,050
500
2.24
2.06
1.97
1564.50
0.03
578.20
0.01
2142.70
0.0414
2779.00
0.05
4921.70
0.10
133.02
145.63
12.61
1080.45
0.03
604.80
0.02
1685.25
0.0434
2084.25
0.05
3769.50
0.10
101.88
113.84
11.96
492.25
0.03
603.40
0.03
1095.65
0.0592
992.50
0.05
2088.15
0.11
56.44
61.00
4.56
2.23
2.05
1.97
1559.60
0.03
578.20
0.01
2137.80
0.0413
57.78
2779.00
75.11
0.05
4916.80
0.09
132.89
136.88
3.99
1076.25
0.03
604.80
0.02
1681.05
0.0433
45.43
2084.25
56.33
0.05
3765.30
0.10
101.76
100.07
-1.69
491.75
0.03
603.40
0.03
1095.15
0.0592
29.60
992.50
26.82
0.05
2087.65
0.11
56.42
61.00
4.58
Appendices
Appendix 51:
A37
Fixed costs
Se nsitivity - mainte nance costs _ -10%
Interest on capital
Depreciation
Truck insurance
Truck licence
Variable costs
Mar 2011
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route/t
R$/t
Variable costs
Apr 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route/t
R$/t
Variable costs
Sep 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/t
R$/t
300,000
7,500
40,000
4,500
3,500
8,000
1,300
R$/year
R$/year
R$/year
R$/month
R$/month
R$/year
R$/year
300,000
7,500
40,000
54,000
42,000
8,000
1,300
152,800
1.91
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
R$
R$/km
Route 1
Sorriso, via MS
2,200
2.33
2560.80
0.03
702.10
0.01
3262.90
0.04
4202.00
0.05
7464.90
0.09
201.75
213.33
11.58
Route 2
Sorriso-MT, via GO
2,100
2.33
2444.40
0.03
578.20
0.01
3022.60
0.04
4011.00
0.05
7033.60
0.09
190.10
213.33
23.23
Route 3
Route 4
Route 5
1,400
1,050
500
Route 1
Sorriso, via MS
2,200
2.31
2545.40
0.03
702.10
0.01
3247.53
0.04
4202.00
0.05
7449.53
0.09
201.34
198.13
-3.21
Route 2
Sorriso-MT, via GO
2,100
2.31
2429.70
0.03
578.20
0.01
3007.93
0.04
4011.00
0.05
7018.93
0.09
189.70
198.13
8.43
Route 3
Route 4
Route 5
1,400
1,050
500
Route 1
Sorriso, via MS
2,200
2.34
2572.90
0.03
702.10
0.01
3275.03
0.04
4202.00
0.05
7477.03
0.09
202.08
185.00
-17.08
Route 2
Sorriso-MT, via GO
2,100
2.34
2455.95
0.03
578.20
0.01
1517.09
0.02
4011.00
0.05
5528.09
0.07
149.41
185.00
35.59
Route 3
Route 4
Route 5
1,400
1,050
500
2.24
2.08
1.97
1564.50
0.03
578.20
0.01
2142.70
0.04
2674.00
0.05
4816.70
0.09
130.18
163.33
33.15
1092.53
0.03
604.80
0.02
1697.33
0.04
2005.50
0.05
3702.83
0.10
100.08
109.33
9.25
493.25
0.03
603.40
0.03
1096.65
0.06
955.00
0.05
2051.65
0.11
55.45
61.00
5.55
2.24
2.06
1.97
1564.50
0.03
578.20
0.01
2142.73
0.04
2674.00
0.05
4816.73
0.09
130.18
145.63
15.45
1080.45
0.03
604.80
0.02
1685.28
0.04
2005.50
0.05
3690.78
0.10
99.75
113.84
14.09
492.25
0.03
603.40
0.03
1095.68
0.06
955.00
0.05
2050.68
0.11
55.42
61.00
5.58
2.23
2.05
1.97
1559.60
0.03
578.20
0.01
2137.83
0.04
2674.00
0.05
4811.83
0.09
130.05
136.88
6.83
1076.25
0.03
604.80
0.02
1681.08
0.04
2005.50
0.05
3686.58
0.09
99.64
100.07
0.43
491.75
0.03
603.40
0.03
1095.18
0.06
955.00
0.05
2050.18
0.11
55.41
61.00
5.59
A38
Appendices
Fixed costs
Se nsitivity - mainte nance costs _ +10%
Interest on capital
Depreciation
Truck insurance
Truck licence
300,000
7,500
40,000
5,500
3,500
8,000
1,300
Variable costs
Mar 2011
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route/t
R$/t
Variable costs
Apr 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route/t
R$/t
Variable costs
Sep 11
Origin
Distance
Local fuel price
Fuel costs
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
km
R$/l
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/route
R$/tkm
R$/t
R$/t
R$/year
R$/year
R$/year
R$/month
R$/month
R$/year
R$/year
300,000
7,500
40,000
66,000
42,000
8,000
1,300
164,800
2.06
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
R$/year
R$
R$/km
Route 1
Route 2
Route 3
Route 4
Route 5
Sorriso, via MSSorriso-MT, via
Rondonópolis-MT
GO
Rio Verde-GO Barretos-SP
2,200
2,100
1,400
1,050
500
2.33
2.33
2.24
2.08
1.97
2560.80
2444.40
1564.50
1092.53
493.25
0.03
0.03
0.03
0.03
0.03
702.10
578.20
578.20
604.80
603.40
0.01
0.01
0.01
0.02
0.03
3262.90
3022.60
2142.70
1697.33
1096.65
0.04
0.04
0.04
0.04
0.06
4532.00
4326.00
2884.00
2163.00
1030.00
0.06
0.06
0.06
0.06
0.06
7794.94
7348.64
5026.74
3860.37
2126.71
0.10
0.09
0.10
0.10
0.11
210.67
198.61
135.86
104.33
57.48
213.33
213.33
163.33
109.33
61.00
2.66
14.72
27.47
5.00
3.52
Route 1
Route 2
Route 3
Route 4
Route 5
Rondonópolis-MT
GO
2,200
2,100
1,400
1,050
500
2.31
2.31
2.24
2.06
1.97
2545.40
2429.70
1564.50
1080.45
492.25
0.03
0.03
0.03
0.03
0.03
702.10
578.20
578.20
604.80
603.40
0.01
0.01
0.01
0.02
0.03
3247.53
3007.93
2142.73
1685.28
1095.68
0.04
0.04
0.04
0.04
0.06
4532.00
4326.00
2884.00
2163.00
1030.00
0.06
0.06
0.06
0.06
0.06
7779.57
7333.97
5026.77
3848.32119
2125.735834
0.10
0.09
0.10
0.10
0.11
210.26
198.22
135.86
104.01
57.45
198.13
198.13
145.63
113.84
61.00
-12.13
-0.09
9.77
9.83
3.55
Route 1
Route 2
Route 3
Route 4
Route 5
Rondonópolis-MT
GO
2,200
2,100
1,400
1,050
500
2.34
2.34
2.23
2.05
1.97
2572.90
2455.95
1559.60
1076.25
491.75
0.03
0.03
0.03
0.03
0.03
702.10
578.20
578.20
604.80
603.40
0.01
0.01
0.01
0.02
0.03
3275.03
1517.09
2137.83
1681.08
1095.18
0.04
0.02
0.04
0.04
0.06
4532.00
4326.00
2884.00
2163.00
1030.00
0.06
0.06
0.06
0.06
0.06
7807.07
5843.11
5021.87
3844.120974 2125.23578
0.10
0.08
0.10
0.10
0.11
211.00
157.92
135.73
103.90
57.44
185.00
185.00
136.88
100.07
61.00
-26.00
27.08
1.15
-3.83
3.56
Appendices
Appendix 52:
A39
Sensitivity analysis - interest rate.
Fixed costs
Interest on capital
i = 0.05
Depreciation
300,000 R$/year
300,000 R$/year
15,000 R$/year
15,000 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
166,300 R$
2.08 R$/km
Variable costs
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.33
2.33
2.24
2.08
1.97
Fuel costs
R$/route
2560.80
2444.40
1564.50
1092.53
493.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3262.90
3022.60
2142.70
1697.33
1096.65
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4573.25
4365.38
2910.25
2182.69
1039.38
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
7836.15
7387.98
5052.95
3880.01
2136.03
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
211.79
199.68
136.57
104.87
57.73
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
1.54
13.66
26.76
4.46
3.27
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
Local fuel price
km
R$/l
2,200
2.31
2,100
2.31
1,400
1,050
500
2.24
2.06
1.97
Fuel costs
R$/route
2545.40
2429.70
1564.50
1080.45
492.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3247.53
3007.93
2142.73
1685.28
1095.68
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7614.53
7176.43
4921.73
3769.53
2088.18
R$/tkm
0.09
0.09
0.10
0.10
0.11
R$/route/t
205.80
193.96
133.02
101.88
56.44
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-7.67
4.17
12.61
11.96
4.56
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Variable costs
0.06 R$/tkm
Distance
Local fuel price
km
R$/l
2,200
2.34
2,100
2.34
1,400
1,050
500
2.23
2.05
1.97
Fuel costs
R$/route
2572.90
2455.95
1559.60
1076.25
491.75
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3275.03
3034.18
2137.83
1681.08
1095.18
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7642.03
7202.68
4916.83
3765.33
2087.68
R$/tkm
0.09
0.09
0.09
0.10
0.11
R$/t
206.54
194.67
132.89
101.77
56.42
R$/t
185.00
185.00
136.88
100.07
61.00
-21.54
-9.67
3.99
-1.70
4.58
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
A40
Appendices
Fixed costs
Interest on capital
i = 0.06
Depreciation
300,000 R$/year
300,000 R$/year
15,000 R$/year
18,000 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
169,300 R$
2.12 R$/km
Variable costs
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.33
2.33
2.24
2.08
1.97
Fuel costs
R$/route
2560.80
2444.40
1564.50
1092.53
493.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3262.90
3022.60
2142.70
1697.33
1096.65
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4655.75
4444.13
2962.75
2222.06
1058.13
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
7918.65
7466.73
5105.45
3919.39
2154.78
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
214.02
201.80
137.99
105.93
58.24
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
-0.69
11.53
25.34
3.40
2.76
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
Local fuel price
km
R$/l
2,200
2.31
2,100
2.31
1,400
1,050
500
2.24
2.06
1.97
Fuel costs
R$/route
2545.40
2429.70
1564.50
1080.45
492.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3247.53
3007.93
2142.73
1685.28
1095.68
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4655.75
4444.13
2962.75
2222.06
1058.13
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
7903.28
7452.06
5105.48
3907.34
2153.80
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
213.60
201.41
137.99
105.60
58.21
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-15.47
-3.28
7.64
8.24
2.79
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Variable costs
0.06 R$/tkm
Distance
Local fuel price
km
R$/l
2,200
2.34
2,100
2.34
1,400
1,050
500
2.23
2.05
1.97
Fuel costs
R$/route
2572.90
2455.95
1559.60
1076.25
491.75
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3275.03
1517.09
2137.83
1681.08
1095.18
R$/tkm
0.04
0.02
0.04
0.04
0.06
R$/route
4655.75
4444.13
2962.75
2222.06
1058.13
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
7930.82
5961.24
5100.62
3903.183474
2153.36078
R$/tkm
0.10
0.08
0.10
0.10
0.12
R$/t
214.35
161.11
137.85
105.49
58.20
R$/t
185.00
185.00
136.88
100.07
61.00
-29.35
23.89
-0.97
-5.42
2.80
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
Appendices
A41
Fixed costs
Interest on capital
i = 0.07
Depreciation
300,000 R$/year
300,000 R$/year
21,000 R$/year
21,000 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
172,300 R$
2.15 R$/km
Variable costs
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.33
2.33
2.24
2.08
1.97
Fuel costs
R$/route
2560.80
2444.40
1564.50
1092.53
493.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3262.90
3022.60
2142.70
1697.33
1096.65
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4738.25
4522.88
3015.25
2261.44
1076.88
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
8001.15
7545.48
5157.95
3958.76
2173.53
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
216.25
203.93
139.40
106.99
58.74
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
-2.92
9.40
23.93
2.34
2.26
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
Local fuel price
km
R$/l
2,200
2.31
2,100
2.31
1,400
1,050
500
2.24
2.06
1.97
Fuel costs
R$/route
2545.40
2429.70
1564.50
1080.45
492.25
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3247.53
3007.93
2142.73
1685.28
1095.68
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4738.25
4522.88
3015.25
2261.44
1076.88
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
7985.78
7530.81
5157.98
3946.72
2172.55
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
215.83
203.54
139.40
106.67
58.72
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-17.70
-5.41
6.23
7.17
2.28
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Variable costs
0.06 R$/tkm
Distance
Local fuel price
km
R$/l
2,200
2.34
2,100
2.34
1,400
1,050
500
2.23
2.05
1.97
Fuel costs
R$/route
2572.90
2455.95
1559.60
1076.25
491.75
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3275.03
1517.09
2137.83
1681.08
1095.18
R$/tkm
0.04
0.02
0.04
0.04
0.06
R$/route
4738.25
4522.88
3015.25
2261.44
1076.88
R$/tkm
0.06
0.06
0.06
0.06
0.06
R$/route
8013.32
6039.99
5153.12
3942.558474
2172.11078
R$/tkm
0.10
0.08
0.10
0.10
0.12
R$/t
216.58
163.24
139.27
106.56
58.71
R$/t
185.00
185.00
136.88
100.07
61.00
-31.58
21.76
-2.39
-6.49
2.29
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
A42
Appendices
Appendix 53
Sensitivity analysis - fuel costs
FUEL +5%
Fixed costs
300,000 R$/year
Interest on capital
Depreciation
300,000 R$/year
7,500 R$/year
7,500 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
158,800 R$
1.99 R$/km
Variable costs
0.05 R$/tkm
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.44
2.44
2.35
2.19
2.07
Fuel costs
R$/route
2688.84
2566.62
1642.73
1147.15
517.91
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3390.94
3144.82
2220.93
1751.95
1121.31
R$/tkm
0.04
0.04
0.04
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7757.94
7313.32
4999.93
3836.20
2113.81
R$/tkm
0.10
0.09
0.10
0.10
0.11
R$/route/t
209.67
197.66
135.13
103.68
57.13
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
3.66
15.67
28.20
5.65
3.87
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.43
2.43
2.35
2.16
2.07
Fuel costs
R$/route
2672.67
2551.19
1642.73
1134.47
516.86
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3374.80
3129.42
2220.96
1739.30
1120.29
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7741.80
7297.92
4999.96
3823.55
2112.79
R$/tkm
0.10
0.09
0.10
0.10
0.11
R$/route/t
209.24
197.24
135.13
103.34
57.10
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-11.11
0.89
10.50
10.50
3.90
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.46
2.46
2.34
2.15
2.07
Fuel costs
R$/route
2701.55
2578.75
1637.58
1130.06
516.34
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3403.68
3156.98
2215.81
1734.89
1119.77
R$/tkm
0.04
0.04
0.04
0.04
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7770.68
7325.48
4994.81
3819.14
2112.27
R$/tkm
0.10
0.09
0.10
0.10
0.11
R$/t
210.02
197.99
134.99
103.22
57.09
R$/t
185.00
185.00
136.88
100.07
61.00
-25.02
-12.99
1.89
-3.15
3.91
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
Appendices
A43
FUEL +10%
Fixed costs
300,000 R$/year
Interest on capital
Depreciation
300,000 R$/year
7,500 R$/year
7,500 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
158,800 R$
1.99 R$/km
Variable costs
0.05 R$/tkm
256.08
244.44
156.45
109.25
49.32
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.56
2.56
2.46
2.29
2.17
Fuel costs
R$/route
2816.88
2688.84
1720.95
1201.78
542.58
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3518.98
3267.04
2299.15
1806.58
1145.98
R$/tkm
0.04
0.04
0.04
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7885.98
7435.54
5078.15
3890.83
2138.48
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
213.13
200.96
137.25
105.16
57.80
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
0.20
12.37
26.08
4.17
3.20
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.55
2.55
2.46
2.26
2.17
Fuel costs
R$/route
2799.94
2672.67
1720.95
1188.50
541.48
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3502.07
3250.90
2299.18
1793.33
1144.90
R$/tkm
0.04
0.04
0.04
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7869.07
7419.40
5078.18
3877.58
2137.40
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
212.68
200.52
137.25
104.80
57.77
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-14.55
-2.39
8.38
9.04
3.23
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.57
2.57
2.45
2.26
2.16
Fuel costs
R$/route
2830.19
2701.55
1715.56
1183.88
540.93
R$/tkm
0.03
0.03
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3532.32
3279.78
2293.79
1788.71
1144.35
R$/tkm
0.04
0.04
0.04
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7899.32
7448.28
5072.79
3872.96
2136.85
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/t
213.50
201.30
137.10
104.67
57.75
R$/t
185.00
185.00
136.88
100.07
61.00
-28.50
-16.30
-0.22
-4.60
3.25
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
A44
Appendices
FUEL +15%
Fixed costs
300,000 R$/year
Interest on capital
Depreciation
300,000 R$/year
7,500 R$/year
7,500 R$/year
40,000 R$/year
40,000 R$/year
5,000 R$/month
60,000 R$/year
3,500 R$/month
42,000 R$/year
Truck insurance
8,000 R$/year
Truck licence
1,300 R$/year
8,000 R$/year
1,300 R$/year
158,800 R$
1.99 R$/km
Variable costs
0.05 R$/tkm
Mar 2011
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.68
2.68
2.57
2.39
2.27
Fuel costs
R$/route
2944.92
2811.06
1799.18
1256.40
567.24
R$/tkm
0.04
0.04
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3647.02
3389.26
2377.38
1861.20
1170.64
R$/tkm
0.04
0.04
0.05
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
8014.02
7557.76
5156.38
3945.45
2163.14
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
216.60
204.26
139.36
106.63
58.46
R$/t
213.33
213.33
163.33
109.33
61.00
Difference
-3.27
9.07
23.97
2.70
2.54
Apr 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.66
2.66
2.57
2.37
2.26
Fuel costs
R$/route
2927.21
2794.16
1799.18
1242.52
566.09
R$/tkm
0.04
0.04
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3629.35
3372.39
2377.41
1847.35
1169.52
R$/tkm
0.04
0.04
0.05
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
7996.35
7540.89
5156.41
3931.60
2162.02
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/route/t
216.12
203.81
139.36
106.26
58.43
R$/t
198.13
198.13
145.63
113.84
61.00
Difference
-17.99
-5.68
6.27
7.58
2.57
Sep 11
Route 1
Route 2
Route 3
Route 4
Route 5
Origin
Sorriso, via MS
Sorriso-MT, via GO
Rondonópolis-MT
Rio Verde-GO
Barretos-SP
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Variable costs
Freight rate
Distance
km
2,200
2,100
1,400
1,050
500
Local fuel price
R$/l
2.69
2.69
2.45
2.26
2.16
Fuel costs
R$/route
2958.84
2824.34
1715.56
1183.88
540.93
R$/tkm
0.04
0.04
0.03
0.03
0.03
R$/route
702.10
578.20
578.20
604.80
603.40
R$/tkm
0.01
0.01
0.01
0.02
0.03
R$/route
3660.97
3402.58
2293.79
1788.71
1144.35
R$/tkm
0.04
0.04
0.04
0.05
0.06
R$/route
4367.00
4168.50
2779.00
2084.25
992.50
R$/tkm
0.05
0.05
0.05
0.05
0.05
R$/route
8027.97
7571.08
5072.79
3872.96
2136.85
R$/tkm
0.10
0.10
0.10
0.10
0.12
R$/t
216.97
204.62
137.10
104.67
57.75
R$/t
185.00
185.00
136.88
100.07
61.00
-31.97
-19.62
-0.22
-4.60
3.25
Toll costs
Variable costs
Fixed costs
TOTAL COSTS
Freight rate
Difference
Appendices
A45
Appendix 54:
Calculation of GHG emissions of soybean shipping from selected regions to
Santos port
Calculation I - Basis
Route
CO 2 equivalent
kgCO2eq/tkm
Origin
Destination
Sorriso-MT
Road
Road-rail
Santos-SP
Rondonópolis
Road
Road-rail -MT
Road
Road
Santos-SP
Rio Verde-GO
Barretos-SP
Santos-SP
Santos-SP
Distance
(km)
truck
0.038
0.038
0.020
0.014
0.038
0.038
0.020
0.014
0.038
0.038
train
2,200
800
1,400
1,400
1,400
200
1,400
1,400
1,050
500
CO 2 equivalent
CO 2 equivalent
CO 2 equivalent
kgCO2eq/t
kgCO2eq/t
kgCO2eq/7000t
per route and ton
per route and ton
per route and 7000t
83.24
30.27
27.44
13.72
52.97
7.57
27.44
13.72
39.73
18.92
83.24
582,702.70
71.43
500,011.89
52.97
48.73
370,810.81
341,092.97
39.73
18.92
278,108.11
132,432.43
Appendix 55:
Sensitivity analysis - increased loading factor (40%) and higher fuel consumption
Calculation II - Increased train loading factor (40%), higher fuel consumption (0.006 l/km)
Route
CO 2 equivalent
kgCO2eq/tkm
Origin
Sorriso-MT
Road
Road-rail
Road
Rondonópolis
Road-rail -MT
Road
Road
Rio Verde-GO
Barretos-SP
Destination
Santos-SP
Santos-SP
Santos-SP
Santos-SP
Source: Author's calculation
Distance
(km)
truck
0.038
0.038
0.020
0.0154
0.038
0.038
0.020
0.014
0.038
0.038
train
2,200
800
1,400
1,400
1,400
200
1,400
1,400
1,050
500
CO 2 equivalent
CO 2 equivalent
CO 2 equivalent
kgCO2eq/t
kgCO2eq/t
kgCO2eq/7000t
per route and ton
per route and ton
per route and 7000t
83.24
30.27
27.44
12.94
52.97
7.57
27.44
11.76
39.73
18.92
83.24
582,702.70
70.65
494,523.89
52.97
46.77
370,810.81
327,372.97
39.73
18.92
278,108.11
132,432.43
A46
Appendices
Appendix 56:
Sensitivity analysis - reduced fuel consumption
Calculation III - Reduced fuel consumption truck (0.45 l/km)
Distance
CO 2 equivalent
CO 2 equivalent
CO 2 equivalent
(km)
kgCO2eq/tkm
kgCO2eq/t
kgCO2eq/t
Route
Origin
Destination
Sorriso-MT
Road
Road-rail
Santos-SP
Rondonópolis
Road
Road-rail -MT
Road
Road
Santos-SP
Rio Verde-GO
Barretos-SP
Santos-SP
Santos-SP
truck
0.034
0.034
0.020
0.014
0.034
0.034
0.020
0.014
0.034
0.034
train
2,200
800
1,400
1,400
1,400
200
1,400
1,400
1,050
500
per route and ton
per route and ton
74.92
27.24
27.44
13.72
47.68
6.81
27.44
13.72
35.76
17.03
CO 2 equivalent
kgCO2eq/7000t
per route and 7000t
74.92
524,432.43
68.40
478,822.70
47.68
47.97
333,729.73
335,795.68
35.76
17.03
250,297.30
119,189.19
Appendix 57:
Sensitivity analysis - decreased emission factor
Route
Origin
Sorriso-MT
Rondonópolis
-MT
Rio Verde-GO
Barretos-SP
Calculation IV - Decreased emission factor (approaching IPCC 2.67)
Distance
CO 2 equivalent
CO 2 equivalent
CO 2 equivalent
(km)
kgCO2eq/tkm
kgCO2eq/t
kgCO2eq/t
Destination
Santos-SP
Santos-SP
Santos-SP
Santos-SP
truck
0.036
0.036
0.019
0.01335
0.036
0.036
0.019
0.01335
0.036
0.036
train
2,200
800
1,400
1,400
1,400
200
1,400
1,400
1,050
500
per route and ton
79.38
28.86
26.17
13.08
50.51
7.22
26.17
13.08
37.89
18.04
per route and ton
CO 2 equivalent
kgCO2eq/7000t
per route and 7000t
79.38
555,648.65
68.11
476,797.05
50.51
46.47
353,594.59
325,256.51
37.89
18.04
265,195.95
126,283.78
Appendices
Appendix 58:
A47
Interview guideline
Company information
Which are the areas of activity of your company?
What is the annual volume of soybeans traded?
Export process
Please describe the export process.
Which are the export routes to Santos and which are the modalities of transportation used?
What is the travelled distance to Santos?
What is the medium time of travelling from origin (warehouse) to destination (port)?
How long does it take to load, travel and unload the vehicle?
Which is the condition of the regional infrastructure and how does the state of conservation of the
streets influence the TC?
Which are the logistic risks in exporting soy and which is the rate of losses per year?
Logistic costs
Please specify the company's costs related to soybean logistics (freight, storage, transshipment, port,
toll, etc.)
Which factor interferes most in the logistic costs (freight, transshipment, port costs) ?
How is the freight price composed and which are major determinants and cost factors?
Does waiting time at the port impact on the transport costs?
Please describe the commercialization process.
Transportation
Which truck is used for soybean transports?
Average utilization of fleet in 1 year (%) ? How many days per year does one truck stand still?
How many journeys does a truck in its util life?
Renovation of fleet - After how many years do you renew your fleet?
Which are the major problems of maintenance?
Is maintenance outsourced?
Which is the emission volume of a truck over one route?
Are the return travels used for the transportation of goods? Please specify.
Storage
Please describe the warehousing infrastructure of the region and of the company.
Please specify the costs of storage for the warehouse operator.
What percentage of farmers have on farm warehouses?
Other
Is bureaucracy a limiting factor to the process flow?
Is there any governmental support?
Please estimate the impact of the new driver's law.
Where do you see major bottlenecks or challenges in the logistics processes?
Please describe the development of the transport sector over the last five to ten years.
Please describe the development of the transport sector over the next five to ten years.
Source: Elaborated by author.
Bibliografische Information:
Die Deutsche Nationalbibliothek
verzeichnet diese Publikationen
in der Deutschen Nationalbibliografie; detaillierte
bibliografische Daten sind im
Internet unter
www.dnb.de abrufbar.
Bibliographic information:
The Deutsche Nationalbibliothek
(German National Library) lists this
publication in the German National
Bibliographie; detailed
bibliographic data is available on
the Internet at www.dnb.de
Zitationsvorschlag – Suggested source citation:
Fliehr O (2013) Analysis of transportation and logistics processes for
soybeans in Brazil. Braunschweig: Johann Heinrich von ThünenInstitut, 173 p, Thünen Working Paper 4
Bereits in dieser Reihe erschienene Bände finden Sie im Internet unter www.ti.bund.de
Volumes already published in
this series are available on the
Internet at www.ti.bund.de
Die Verantwortung für die
Inhalte liegt bei den jeweiligen
Verfassern bzw. Verfasserinnen.
The respective authors are
responsible for the content of
their publications.
Herausgeber/Redaktionsanschrift – Editor/address
Johann Heinrich von Thünen-Institut
Bundesallee 50
38116 Braunschweig
Germany
[email protected]
www.ti.bund.de
DOI:10.3220/WP_4_2013
urn:nbn:de:gbv:253-201307-dn052162-2

Analysis of transportation and logistics processes for soybeans in

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